Processing Power Research Articles

Reducing Makespan and Enhancing Resource Usage in Cloud Computing With ESJFP Method: A New Dynamic Approach

ABSTRACTA well‐designed web‐based tool or application allows consumers to access on‐demand services on a pay‐per‐use basis via the internet in the cloud computing service paradigm. Cloud computing continues to be a leading technology trend, with a primary focus on optimizing and enhancing end‐user applications. The increasing challenge of meeting the diverse needs of clints for service providers is propelling the development of load scheduling algorithms. Some of the current scheduling algorithms used in cloud computing include First Come First Serve (FCFS), Shortest Job First (SJF) and Round Robin (RR). The response time and makespan—the interval between the start and end times of consecutive tasks on the same machine—are the two most crucial load balancing elements. Response time refers to the duration a server takes to respond to a client's request. Measured in milliseconds, this timer starts when a client sends a request and stops when the server sends its initial response. This study examines many load balancing methods and suggests improvements for cloud computing's Shortest Job First (SJF) methodology. To minimize makespan and maximize resource usage, we provide a solution that is Enhanced Shortest Job First with Priority (ESJFP) load scheduling algorithms. Under the ESJFP method, computers with more processing power are assigned the longest tasks with higher MIPS (million instructions per second) needs, while machines with lesser processing capacity are assigned the shortest jobs with lower MIPS requirements. By giving equal priority to all activities, this technique makes sure that neither high‐MIPS nor low‐MIPS jobs have to wait an extended period of time for resource allocation.

Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices

AbstractIntra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single‐neuron spike detection require high processing rates and power, hindering the up‐scaling for neurons‐population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold‐based neural activity detection. MIS leverages analogue multi‐state switching properties of metal‐oxide RRAM to compress neural inputs by encoding above‐threshold events in resistance displacement, facilitating efficient data down‐sampling in the post‐processing, enabling low‐power, high‐channel systems. Initially tested on spikes and local field potentials, here MIS is adapted to process multi‐unit activity envelope (eMUA)—the envelope of entire spiking activity—which has recently been proposed as crucial input for real‐time neuro‐prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal‐oxide devices: Pt/TiOx/Pt and TiN/HfOx/TiN, proving its platform‐agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, it is shown that it can reduce total system power consumption to below 1 µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional complementary metal‐oxide‐semiconductor (CMOS) front‐ends. This eMUA‐MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use.

Formal Verification with ABV : A Superior Alternative to UVM for Complex Computing Chips

This article explores the evolution and effectiveness of formal verification enhanced with Assertion-Based Verification (ABV) as a superior alternative to traditional Universal Verification Methodology (UVM) in complex computing chip design. Through analysis of implementation data from major semiconductor companies, including Intel's Core i7 and IBM's POWER processors, the article demonstrates how formal methods achieve up to 100% coverage of critical modules compared to UVM's typical 80-85% coverage. The research presents quantitative evidence of formal verification's advantages, including a 25-30% reduction in verification cycles, a 40% increase in security vulnerability detection, and a 55% reduction in overall verification time through advanced techniques such as compositional verification and proof decomposition. The article also addresses scalability challenges and resource optimization strategies, providing a comprehensive framework for implementing formal verification in modern chip design workflows.

A Blockchain based Secure Authentication Technique for Ensuring User Privacy in Edge Based Smart City Networks

In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth consumption, scalable, real-time decision-making, security, reduced network congestion, and increased resilience. Despite these benefits, EC networks face persistent challenges, particularly related to security and privacy concerns. Addressing these security challenges requires strong authentication mechanisms, which demand extra resources like processing power and memory, often surpassing the limited capabilities of lightweight edge devices compared to cloud systems. This highlights the critical need for securing edge nodes and ensuring user privacy before real-world deployment and data transfer. User and edge device authentication is vital to prevent external and internal Impersonation and Reflection attacks that threaten system integrity and confidentiality. This paper presents a BlockChain based Authentication technique for Edge Networks (BCAuthEN) that utilizes a Consortium Blockchain (CB) with key agreements for biometric authentication, incorporating a Fuzzy Extractor (FE) to secure user biometrics and passwords. In addition, BCAuthEN offers multifactor and continuous authentication by monitoring user behavior and biometrics. BCAuthEN has been formally verified through Real-Or-Random (RoR) modeling and AVISPA tool, proving its effectiveness in enhancing privacy, and security. The proposed technique ensures robust security by preventing attackers at the potential entry points (edge nodes). In addition, BCAuthEN reduces computation cost, communication overhead and improves throughput. BCAuthEN provides strong resilience by achieving high detection accuracy and reduces false positives against impersonation and reflection attacks. Results have shown that BCAuthEN improves communication costs and reduces overhead by 10% and 7%, respectively, as compared to the recent biometric and key-based user authentication techniques.

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks

A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.

Quantum Entanglement and Qubit Interactions: The Key to Quantum Supremacy

Abstract. Quantum computing operates in a fundamentally different way from classical computing by harnessing the principles of quantum mechanics to process information. Quantum supremacy is achieved when a quantum computer can solve problems that are beyond the capabilities of classical systems, including the human brain, showcasing its superior processing power. To attain quantum supremacy, quantum entanglement and qubit interactions play a pivotal role. Quantum entanglement occurs when qubits are interconnected in a manner where the state of one qubit directly influences the state of others, enabling the quantum computer to perform multiple operations simultaneously. Moreover, effective interaction between qubits is essential for the performance of complex calculations in quantum systems, highlighting the significance of coherence and error correction. Understanding the importance of coherence in preventing and rectifying errors in quantum computations is crucial. This paper aims to explore the critical aspects of quantum entanglement and qubit interactions, which are foundational to the operation of quantum computers. By delving into these key concepts, the paper aims to elucidate their significant roles in achieving quantum supremacy. The discussion will center on how quantum entanglement, which allows enhanced computational parallelism through qubit interconnection, and efficient qubit interactions vital for complex computations, contribute to surpassing the capabilities of classical computers. Comprehending these principles is crucial for advancing quantum computing technology and overcoming the challenges to unleash its full potential.

Using Time-Series Databases for Energy Data Infrastructures

The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.

Enhancing IOT Security: Investigating Lightweight Encryption Algorithms for Resource-Constrained Devices

Abstract: In this work, we examine three LWCs: namely AES-128, SPECK, and ASCON, considering their presentation on resource-constrained IoT devices. With the large-scale deployment of IoT in all areas of industry, ensuring the security and efficiency of cryptographic implementations has become highly critical because typical IoT devices are usually characterized by limited memory, processing power, and energy resources. The study ascertains the memory usage, throughput, energy efficiency, and strength of security in the chosen algorithms by highly popular resource-constrained boards such as Arduino Nano and Arduino Micro. The benchmarks have shown that SPECK has the highest throughput, which is much efficient for constrained environments. One of the significant features of ASCON is that it has minimized memory usage and high energy efficiency. Therefore, it suits for long-term usage in IoT. AES-128 is secure but resource-intensive, so its usage may be irrelevant in very constrained environments. The results are of great value for IoT developers as a contribution to the selection of cryptographic algorithms based on the conditions and resource restrictions of the IoT devices involved so that optimized security solutions are achieved.

Image Deblurring Using Non Linear Activation Free Network

Abstract: While picture restoration has made significantstrides in recent years, the sophisticated techniques employed are becoming more intricate. This makes a proper comparison difficult. In this research, we propose a straightforward approachthat outperforms these intricate ones in terms of accuracy while requiring less computational resources. We discovered that complex activation functions such as GELU, Softmax, ReLU, andSigmoid are not really needed. Therefore, we developed a more basic version that does not need them, which we call NAFNet.We put our approach to the test on challenging tasks such as noise reduction and image correction. It performed much better, far better than the sophisticated techniques, and used a lot less processing power.

Technology-Enhanced Art and Sustainable Discourse Practices

This study explored the potential of technology-enhanced art in fostering sustainable discourse practices and social connection in the digital age. As technology advances, new opportunities emerge to integrate digital tools into creative practices, particularly in the realm of art. By examining the intersection of art, technology, and discourse analysis through a sustainability lens, this research aims to uncover innovative approaches to promoting effective communication, civic engagement, collaboration, intercultural understanding, empathy, and resilience incorporating co-design processes, shared decision-making, or collaborative art-making activities. The study provides an overview of technology-enhanced art practices including social media, virtual reality, interactive installations, and digital storytelling tools, and their impact on discourse practices within diverse socio-cultural contexts. It investigates the role of digital platforms and creative processes in facilitating social connection through discourse. The research focuses on a thematic approach to technology-enhanced art interventions that promote sustainable discourse practices and social connection. Through a thematic and intervention-based approach, this research combines a review of current literature with case studies showcasing the successful implementation of technology-enhanced art interventions. So the current study argues that technology-enhanced art holds significant potential for fostering sustainable discourse practices and social connection in the digital age. By harnessing the power of digital tools and creative processes in an environmentally conscious manner, artists, educators, and practitioners can support social connections. This, in turn, contributes to personal, social, and environmental resilience in a rapidly evolving technological landscape.

Phonetic Universals

Understanding the range and limits of crosslinguistic variation stands at the core of linguistic typology and basic science. Linguistic typology is concerned with the relevant dimensions along which languages can vary and those along which they remain stable; an overarching goal is to understand the cognitive, physical, social, and historical factors that shape language. Phonetics is no exception to this enterprise, but it has faced obstacles in crosslinguistic data collection and processing power. The field has nevertheless established a solid foundation regarding the relevant dimensions of stability, revealing strong phonetic tendencies across languages (i.e., universals). This article provides an overview of phonetic universals with a summary of previously attested descriptive and analytic phonetic universals and consideration of methodological aspects when investigating phonetic universals. The increasing availability of multilingual speech data along with advanced speech processing tools promises a new era for investigations into crosslinguistic phonetic variation and systematicity.

Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures

Due to the ongoing promotion of the latest technological revolution and industrial upgrading, as well as the widespread use of artificial intelligence and deep learning, the intelligent Internet of Things terminal has reached a more advanced stage of development. The combination of training data, model algorithms, and processing power is the basis for advancing artificial intelligence. The semiconductor business plays a crucial role in shaping the future of AI algorithms and the computing power industry. This study explores the progression of artificial intelligence, and essential technologies, and examines the categorization and structure of AI chips. The process of deep learning was then analyzed, demonstrating the application of AI in real life. Next, this study showcases distinct AI chip-related models, specifically designed for the automated implementation of Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). The study then proceeds to examine the unique procedure and the significance of the resulting data. The future of AI was examined in the domains of space exploration, healthcare, and autonomous driving, along with future forecasts.

Parents’ perceptions of power in the school exclusion process examined through Arnstein's ladder of participation

ABSTRACT Parents have highlighted that they felt powerless in the school exclusion process because school staff speak louder and have all the authority. This study draws on Arnstein's ladder of citizen participation to analyse parents’ experiences of the school exclusion process. At the bottom of Arnstein's ladder is nonparticipation; citizens cannot participate in decision-making in this domain. The following domain is tokenism. This is where citizens have a voice and are heard, but their views are not considered. The final domain at the top of the ladder is citizen control; this is where citizens have varying degrees of decision-making power. This paper uses data from semi-structured interviews with parents to understand their children's experiences of school, measures that schools implemented to prevent exclusions, and how they experienced the school exclusion process. The data showed that parents could feel manipulated in the school exclusion process when they were involved too late when school staff had already decided to exclude their children. Conversely, some parents felt that they had the power to influence decisions and valued working in partnership with school staff to inform the sanction their child would receive and avoid permanent exclusion. It is concluded that not all parents have equal influence in the school exclusion process. The findings of this research highlight the need to make sure that all parents are given the opportunity to participate in the exclusion process to ensure that the education system is fairer and more inclusive.

Design and Implementation of Digital Integrated Circuit Based CPU

This paper presents a simplified 16-bit CPU design and its implementation using digital integrated circuits developed with Verilog hardware description language, while emphasizing simulation through the EDA tool. This design puts more emphasis on the critical roles played by the data path and the control unit, executing key instructions including arithmetic operations such as addition, subtraction, bitwise logic, and conditional jumps. This structurally modular CPU is both functioning and stable in nature, allowing support for future enhancements and enhancements as well, thereby serving as an effective model towards an understanding of the principles of CPU design. The execution of these basic instructions puts the core of the CPU under test and reaffirms its proper operation in respect of the detailed instruction set, while its extensibility design confirms further improvement possibilities for future complex instructions as well as finite-state machine optimization for the control unit. The present work not only proves the possibility of creating a fully working CPU, using readily available bare minimum resources to implement a prototype, but is also a breakthrough in future research with CPU architecture. The obtained results will shed light on CPU functionality optimization and extension, being the ground for building more powerful and effective processors in the future.

Comparison of microwave and pulsed electric field methods on extracting antioxidant compounds from Arvaneh plant (Hymenocrater platystegius Rech. F)

Improving the quality of extracts derived from medicinal plants is a critical concern due to their extensive use across various industries. The Arvaneh plant (Hymenocrater platystegius Rech. F), a species native to the Khorasan province in Iran and belonging to the mint family, was the focus of this study. We optimized the extraction of Arvaneh plant using both microwave (MW) and pulsed electric field (PEF) techniques. The MW method was tested at different processing times (90, 180, and 270 s) and power levels (180, 540, and 900 W), while the PEF method was evaluated with varying electric field intensities (0.25, 3.25, and 6.25 kV/cm) and pulse numbers (10, 45, and 80) using a central composite design (CCD). The results revealed that extraction efficiency, total phenolic content, and total flavonoid content were significantly higher with the PEF method compared to the MW method (p < 0.05). Moreover, the PEF technique showed superior performance in preserving the antioxidant properties of the extract, as assessed by DPPH and FRAP methods. GC/MS analysis confirmed the presence of 27 bioactive compounds in the Arvaneh extract obtained through PEF-assisted extraction. In conclusion, the PEF method proved to be highly efficient for extracting bioactive compounds from the Arvaneh plant.

A Comprehensive of CPU and GPU Performance and Applications in Autonomous Vehicles

The rapid advancement of autonomous vehicle technology over the past decade has significantly increased the complexity of intelligent transportation systems. This need is clearly reflected in the requirements for high-performance computing in autonomous vehicles — a requirement that artificial intelligence, machine learning and big data analytic have all come to meet through their integration. We need the CPU and GPU power to handle processing in real-time data, sensor fusion and decision-making. We investigate how the CPU and GPU perform in an autonomous driving scenario, concluding that these tasks are complementary to one another describing where each can be best used within a heterogeneous computing architecture. The research delves into how these computing chips can be best used under the demand for real-time processing, reliability and efficiency. The study is to better understand the techniques for improving CPU-GPU collaboration, and applies these new findings on performance-intensive tasks like image recognition or track construction. The technical part involves conducting driving scenarios in the real world on three different types of CPU and several GPU, with key metrics such as processing latency, power consumption and accuracy. The actual experimental results show the advantages of GPU parallel computing and deep learning, while CPU still has a good advantage in multi-tasking ability and logical calculation. The results are important for the implementation of future autonomous driving systems, highlighting heterogeneous computing architectures as a means to optimize and support safe, effective vehicle operations.

Optimizing Test Data Management Strategies in Banking Domain Projects

The steady rise in processing power over the past 20 years has resulted in an enormous volume of data. Furthermore, anybody may now easily create and consume material in any format thanks to recent advancements in Web technology. Large volumes of data are regularly gathered by banking systems, including trade finance data, SWIFT and telex communications, client information, details about transactions, risk profiles, credit card details, limit and collateral details, the compliance or Anti Money Laundering (AML)-related data, and limit and collateral details. Every day, thousands of choices are made at banks. These choices pertain to credit, default, beginning a relationship, investments, AML, and illicit funding, among other things. To make these crucial choices, one must rely on a variety of data and drill down capabilities offered by the banking systems. We created a set of specifications for the kinds of data that should be included in a product catalogue. We ascertained what data the departments need by using a survey and questionnaire of the retailer's staff. We ensured that there was no one standard for the information organisation and then put out our own plan. This enormous amount of data may be mined for information and intriguing patterns, which can then be used to the decision-making process. This article examines and summarises a number of data mining methods that have applications in the banking industry. An overview of data mining methods and procedures is given. It also sheds light on how these methods may be applied in the banking industry to facilitate and enhance decision-making.

In at the Deep End: Innovative approaches to engaging underserved communities in Northern England

Abstract Background People living in socio-economically deprived communities have greater health needs, but worse access and outcomes from care. A growing international movement of Deep End (DE) general practice networks established to promote health equity, address challenges posed by the inverse care law and advocate for increased research involvement. Researchers, community members and practitioners in the North of England are exploring ways of engaging with underserved communities and addressing low levels of inclusion in research. Methods DE research i) co-designing the North East and North Cumbria (NENC) DE network with practitioners to prioritise service investment, research activities and initiatives for local communities; ii) Deep End Research Alliance Yorkshire and Humber (DERA-YH) and Participatory Action Research to increase research capacity and reflect upon the evolution of a novel primary care Community Research Link Worker role (CRLWs). Results Prioritisation activities in the NENC DE network, uncovered the volume and complexity of clinical and social need, leading to the co-design and evaluation of initiatives to improve mental health care, increase childhood immunisations, tailor deprescribing, support training, and increase patient inclusion in research. In the DERA-YH, the CRLW model increased participation in research from ethnic minority communities regarding prostate cancer, lung health, dementia, and contraceptive services. Community leaders stated that sharing resources and power in the research process builds trust and interest in research in their communities. Conclusions DE networks in Northern England have driven innovative approaches to engage underserved communities in research, fostering empowering and enduring relationships to address health and care inequalities. Co-design methodologies and CRLWs integration into research teams had a transformative impact on the accessibility of research for communities and for the researchers’ positionality. Key messages • Deep End networks are advancing approaches to meaningful engagement and partnership working with underserved communities, to address health, care and research participation inequalities. • We recommend co-design by default and CRLW models to build capacity and embed reflective practice, mutual respect, and power sharing across the research team.

All‐Optical Imaging Using Perovskite Nanocrystals Based on Spectro‐Spatial Correlation

AbstractThe exponential growth of data from sensors and internet‐connected devices has challenged traditional electronic computing, particularly in processing speed and power efficiency. Optical information processing, with its inherent high speed and parallelism, offers a promising solution. Moreover, optical processing is vital for developing all‐optical networks, where all‐optical imaging is essential. In this paper, a novel all‐optical imaging technique that leverages in situ anion exchange reactions between perovskite nanocrystals to create an array of pixels with tunable emission wavelengths is proposed. This method enables the conversion of UV light, whether transmitted or reflected by an object, into the visible spectrum, thereby establishing a direct correlation between spectral and spatial information. By employing wavelength division multiplexing (WDM), the approach reduces the dimensionality of information acquisition from two dimensions to one, enhancing imaging speed. This innovation paves the way for significant advancements in all‐optical imaging and image processing, offering new possibilities for faster and more efficient imaging technologies.