Computing Applications Research Articles

RISC-V Leads the Future: Evolution and Outlook of CPU Architecture

This essay mainly talked the development of CPU and some instruction set architecture based on RISCV. CPU is known as the Central Processing Unit, the main appliment is RISCV, Also the ARM architecture is characterized by a focus on the balance between low power consumption and high performance. Then X86, its significant advantage is its complex instruction set and excellent performance, so it can handle complex computational tasks. We also explore the Pipeline technology, it is a widely used parallel processing technology today. The design principle is to divide complex, multilevel combinational logic circuits into multiple levels. Then, we find some experiment data to explore our conclusion. We find that each has its own advantages and is better suited to different scenarios. In terms of performance, x86 offers superior capabilities but consumes more power, making it ideal for high-performance computing and server applications. ARM, on the other hand, excels in power efficiency and finds its primary use in mobile devices and embedded systems. RISC-V, known for its flexibility, allows for a balance between performance and power consumption based on specific requirements, making it suitable for highly customizable applications, IoT devices, and emerging high-performance computing markets. The advantages of each architecture in different fields depend on the specific application environment and demands placed on them.

Tunable Liquid Crystal Twisted‐Planar Fresnel Lens for Vortex Topology Determination

AbstractThe development of straightforward and effective methods to determine the topological charge of phase singular beams is crucial for broadening the applications of optical vortices. Here, a straightforward and elegant method for determining the topological charge of an optical vortex using an innovative switchable achromatic nematic liquid crystal Fresnel lens is proposed. The approach allows for the unambiguous determination of both the absolute value and the sign of the topological charge of a singular beam across a wide range of the visible spectrum, as demonstrated both experimentally and theoretically. The research outcomes are promising for applications in optical information transmission systems, cryptography, and quantum computing.

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library that uniquely offers: (1) a straightforward syntax for programming productivity, (2) usable data structures for data-oriented programming (DOP) for performance, and (3) a simple interface to the open-source C++ Kokkos library for portability and memory management across CPUs and GPUs. The portability across architectures with a single code implementation is achieved by automatically switching between diverse fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. The MATAR library solves many longstanding challenges associated with easily writing software that can run in parallel on any computer architecture. This work benefits projects seeking to write new C++ codes while also addressing the challenges of quickly making existing Fortran codes performant and portable over modern computer architectures with minimal syntactical changes from Fortran to C++. We demonstrate the feasibility of readily writing new C++ codes and modernizing existing codes with MATAR to be performant, parallel, and portable across diverse computer architectures.

Current Trends in ICT Security: A Comprehensive Overview of Machine Learning and Soft Computing Applications

In an era marked by pervasive digitization, information and communication technology (ICT) security stands as a cornerstone of our digital infrastructure. The rapid evolution of cyber threats necessitates equally agile defence mechanisms. This research paper provides a comprehensive overview of the contemporary landscape of ICT security with a specific focus on the emergent trends in machine learning (ML) and soft computing (SC) applications. The paper commences by outlining the escalating challenges posed by cyber adversaries, underlining the inadequacy of traditional security paradigms in contending with the intricacies of modern-day attacks. This backdrop emphasizes the need for innovative, adaptable, and intelligent approaches to bolster ICT security.

Reverse-Engineered Exact Control of Population Transfer in Lossy Nonlinear Three-State Systems

We introduce a reverse-engineered scheme for achieving the precise control of population transfer in nonlinear quantum systems characterized by a 1:2 resonance. This scheme involves the use of two resonant laser pulses that transition from initial and final states to an intermediate level exhibiting irreversible losses. In comparison to alternative techniques, our approach offers computational efficiency advantages. Notably, the analytically defined form of the pump pulse enables tailored control strategies, enhancing robustness against decoherence and imperfections. This flexibility extends to choosing dump pulses and designing time evolution scenarios. These features open doors for practical implementation and scalability in quantum technologies, with potential applications in quantum information processing, quantum computing, and quantum communication.

Overview of homomorphic encryption technology for data privacy

This study examines the overview of homomorphic encryption technology for data privacy. In the era of big data, the growing need to utilize vast amounts of information while ensuring privacy and security has become a significant challenge. Homomorphic encryption technology has gained attention as a solution for privacy-preserving data processing, allowing computations on encrypted data without exposing sensitive information. This study introduces the concept of data privacy preservation and explores the evaluation of homomorphic encrypted technology. The focus is on analyzing both partial and full homomorphic encryption methods, highlighting their respective characteristics, evaluation criteria, and the current state of research. Partial homomorphic encryption supports limited operations, while full homomorphic encryption enables unlimited computation on encrypted data, though both face challenges related to computational overhead and efficiency. Additionally, this paper addresses the ongoing issues and limitations associated with homomorphic encryption, such as its complexity, large encryption volumes, and difficulties in handling large-scale datasets. Despite these challenges, researchers continue to refine the technology and expand its applications in cloud computing, big data analytics, and privacy-preserving computing environments. This study also discussed potential future research avenues aimed at improving the scalability, efficiency, and security of homomorphic encryption to support broader, real-world applications. Ultimately, homomorphic encryption is positioned as a key enabler for secure data utilization in an increasingly privacy-conscious digital landscape.

Acceptability and quality of the 'Grog Survey App' brief intervention: Helping Aboriginal Australians reflect on their drinking using a digital health tool.

The Grog Survey App is a validated, visual and interactive self-administered application for tablet computers that is designed to help Aboriginal Australians describe their alcohol consumption. Each person who completes the App also receives a brief intervention with feedback tailored to their survey responses. We aimed to qualitatively assess the acceptability and perceived quality of the Grog App's brief intervention, among higher risk consumers and health providers at an Aboriginal residential rehabilitation centre. This descriptive qualitative study analysed feedback from clients (n = 20) and staff (n = 10) of a drug and alcohol residential rehabilitation service on the brief intervention element of the Grog App. Data were collected face-to-face via semi-structured interviews over four consecutive weeks between May and June 2021. A content analysis was conducted, which was informed by the Mobile App Rating Scale (MARS). Client and staff feedback is summarised using four themes from the MARS framework: (i) aesthetics; (ii) engagement; (iii) functionality; and (iv) information. Most clients and staff felt like health messages on the brief intervention were written by 'someone who understands'. Overall, clients and staff described the brief intervention as visually appealing, engaging and likely able to elicit 'lightbulb moments'. The brief intervention on the Grog App is unique in its provision of tailored advice based on survey responses to all individuals (i.e., those who do not drink through to those with likely dependence). Further research is needed to assess effectiveness of this brief intervention.

Flexible TiO2-WO3−x hybrid memristor with enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

Tungsten oxide (WO3)-based memristors show promising applications in neuromorphic computing. However, single-layer WO3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO3−x and TiO2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO2 not only enhances the memristor’s memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The “learning-forgetting-relearning” characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%.

Quantum Machine Learning for Advanced Data Processing in Business Analytics: A Path Toward Next-Generation Solutions

Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.

A Conceptual Level on Green Computing Applications on Eco-Friendly Approaches

The Green computing will work in this field as a life saver to environment as green computing will reduce energy usage by using various green computing eco-friendly approaches like-green design, green awareness, green standards, green disposals, green manufacturing and green usage which therefore help in the less emission. The "Going Green" is a rising trend establishing itself as the preferred way of doing things while saving the environment. This now appears in a large number of aspects in our lives, such as recycling, energy-efficient devices, clean energy sources, eco-friendly vehicles, green buildings. Computing has also established its share to contribute to saving the environment under the concept "Green Computing". Green computing is the environmentally responsible and eco-friendly use of computers and their resources. In broader terms, it is also defined as the study of designing, engineering, manufacturing, using and disposing of computing devices in a way that reduces their environmental impact. Green Computing, also known as Green Technology or Green IT, has quickly emerged as the most effective means of utilising technology.

Machine Learning Techniques Towards Accurate Emotion Classification from EEG Signals

This article delves into using machine learning algorithms for emotion classification via EEG brain signals. The goal is to discover an accurate model beyond traditional methods, necessitating AI for classifying emotional EEG signals. This study, motivated by the complex link between emotions and neural activity, employs Random Forest, Support Vector Machines, and K-Nearest Neighbors. Notably, Random Forest achieves 99% accuracy, SVM 98%, and KNN 94%. These impressive results, backed by performance metrics like confusion matrices, reveal each model’s effectiveness in emotion classification. The dataset, rich in varied emotional stimuli and EEG placements, provides a robust foundation for detailed analysis. This research underscores significant applications in affective computing and mental health, offering a promising path to understanding the intricate relationship between EEG signals and human emotions.

Satellite dish-like nanocomposite as a breakthrough in single photon detection for highly developed optoelectronic applications

A nanocomposite with a unique satellite dish-like structure, termed arsenic (III) oxide iodide (AsO2I)/polypyrrole (Ppy) intercalated with iodide ions (AsO2I/Ppy-I), has been meticulously developed via a two-step process. It features natural satellite dish-like nanostructures, potentially serving as nanoresonators for efficient single photon absorption. With a bandgap of 2.8 eV, the AsO2I/Ppy-I nanocomposite efficiently absorbs photons in the UV and visible light regions, making it suitable for single photon detection. Impressive performance is seen in photocurrent sensitivity measurements, recording values of 0.017 mA.cm−2 under white light and 0.009 mA.cm−2 under monochromatic light at 340 nm. Additionally, it exhibits high responsivity and detectivity, with peak values at wavelengths of 340 nm and 440 nm associated with the diameter of the Satellite dish nanostructure. Cost-effectiveness and simple synthesis methods make it attractive for industrial applications, while its unique structural characteristics and enhanced optical properties position it as a valuable asset in optoelectronic technologies. It holds promise as a leading material in advanced quantum technology, marking a significant leap forward in optoelectronic technologies, with potential applications in quantum cryptography, communication, and computing.

Spatial photoinduced doping of graphene/hBN heterostructures characterized by quantum Hall transport

Abstract Doped semiconductors are a central and crucial component of all integrated circuits. By using a combination of white light and a focused laser beam, and exploiting hexagonal boron nitride (hBN) defect states, heterostructures of hBN/Graphene/hBN are photodoped in-operando, reproducibly and reversibly. We demonstrate device geometries with spatially-defined doping type and magnitude. After each optical doping procedure, magnetotransport measurements including quantum Hall measurements are performed to characterize the device performance. In the unipolar (p+–p–p+ and n–n+–n) configurations, we observe quantization of the longitudinal resistance, proving well-defined doped regions and interfaces that are further analyzed by Landauer–Buttiker modeling. Our unique measurements and modeling of these optically doped devices reveal a complete separation of the p- and n-Landau level edge states. The non-interaction of the edge states results in an observed ‘insulating’ state in devices with a bi-polar p–n–p configuration that is uncommon and has not been measured previously in graphene devices. This insulating state could be utilized in high-performance graphene electrical switches. These quantitative magnetotransport measurements confirm that these doping techniques can be applied to any two-dimensional materials encapsulated within hBN layers, enabling versatile, rewritable circuit elements for future computing and memory applications.

A Study of q-Deformed Bosons, and Their Implications to Quantum Optics

In this study, we investigate three types of q-deformed boson oscillators, focusing on their mathematical frameworks and thermodynamic properties. We calculate key thermodynamic quantities, such as internal energy and entropy, as functions of the deformation parameter q. Our results reveal that these oscillators are eigenstates of specific deformed boson annihilation operators. We also analyze their unique characteristics and implications in deformed quantum optics. Furthermore, we examine the impact of q-deformation on qutrit logic gates, including cycle, self-shift, controlled cycle, controlled self-shift, Feynman, ternary Toffoli, and Fredkin gates, highlighting their altered computational properties. This research contributes to a deeper understanding of q-deformed systems and their applications in quantum computing. Overall, it opens new avenues for exploring the interplay between deformation parameters and quantum information processing.

Robust topological insulating property in C2X-functionalized III-V monolayers

Two-dimensional topological insulators (TIs) show great potential applications in low-power quantum computing and spintronics due to the spin-polarized gapless edge states. However, the small bandgap limits their room-temperature applications. Based on first-principles calculations, a series of C2X (X = H, F, Cl, Br and I) functionalized III-V monolayers are investigated. The nontrivial bandgaps of GaBi-(C2X)2, InBi-(C2X)2, TlBi-(C2X)2and TlSb-(C2X)2are found to between 0.223 and 0.807 eV. For GaBi-(C2X)2and InBi-(C2X)2, the topological insulating properties originate from thes-px,yband inversion induced by the spin-orbital coupling (SOC) effect. While for TlBi-(C2X)2and TlSb-(C2X)2, the topological insulating properties are attributed to the SOC effect-induced band splitting. The robust topological characteristics are further confirmed by topological invariantsZ2and the test under biaxial strain. Finally, two ideal substrates are predicted to promote the applications of these TIs. These findings indicate that GaBi-(C2X)2, InBi-(C2X)2, TlBi-(C2X)2and TlSb-(C2X)2monolayers are good candidates for the fabrication of spintronic devices.

An efficient and improved algorithm for generating an equivalent planar architecture of the data vortex switch

The data vortex (DV) switch was originally designed as an all optical interconnection network for high performance computing (HPC) applications. It was highly scalable with low latency and high throughput, compared to traditional switches used for optical packet switching (OPS) and HPC applications. The equivalent planar (chained MIN) model of the DV, proposed in previous literature, is a planar diagrammatic conversion of a 3-D model of the DV network. In this paper, we will focus on exploring a new, efficient and improved generalized algorithm that enables the arrangement of routing pathways to be implemented as an equivalent planar architecture (EPA, chained MIN) of a DV to find all possible routes between each source and target node pair. The original 3-D architecture of any size can be converted to a planar structure more simply by using the new generalized equivalent planar architecture (EPA) algorithm. To verify the proposed EPA algorithm, it was tested with different input traffic loads and network sizes while keeping the active angle ‘A’ constant. Network performance parameters, including injection rate (throughput) and latency (mean hops), obtained from simulation results are also provided to confirm the validity of the proposed EPA algorithm. This new algorithm is versatile, simple and can be applied to networks of various sizes.

Design of energy-efficient full subtractor circuit at near threshold computing for signal processing application

Abstract Energy Efficiency is a critical factor while designing integrated circuits. Therefore, a 1-bit full subtractor (FS) cell is proposed for lower power application by employing Gate Level Body Biasing (GLBB) scheme for Near Threshold Computing (NTC) application to conquer a unique module for achieving full swing borrow output.We evaluate power, delay, energy and the product of energy with delay (EDP) metrics with respect to C-CMOS full subtractor. The proposed feedback based with FS with GLBB technique has a total die area of 60.02 μm2, while the average power, delay, and energy are 1138 pW, 242 ns, and 27.53 aJ, respectively. The results revealed that our proposed subthreshold hybrid FS circuit with GLBB scheme successfully achieved more than 10.46% average power consumption, 26.58% energy consumption reductions, and 17.98% EDP savings compared to conventional CMOS configuration and other hybrid counterparts. GLBB circuits with FS achieve performance levels that are not affordable in C-CMOS, DTMOS,and GLBB with full adder configurations. Therefore, the FS circuit serves as an efficient divider circuit in terms of detecting objects for image processing applications.

Innovative Scientific Discoveries: The Role of Intelligent Computing in the Fifth Paradigm Shift

This article explores the key role of intelligent computing in driving the paradigm shift of scientific discovery. The article first outlines the five paradigms of scientific discovery, from empirical observation to theoretical models, then to computational simulation and data intensive science, and finally introduces intelligent computing as the core of the fifth paradigm. Intelligent computing enhances the ability to understand, predict, and automate scientific discoveries of complex systems through technologies such as deep learning and machine learning. The article further analyzes the applications of intelligent computing in fields such as bioinformatics, astronomy, climate science, materials science, and medical image analysis, demonstrating its practical utility in solving scientific problems and promoting knowledge development. Finally, the article predicts that intelligent computing will play a more critical role in future scientific research, promoting interdisciplinary integration, open science, and collaboration, providing new solutions for solving complex problems.

Nonlocal photonic quantum gates over 7.0 km

Quantum networks provide a prospective paradigm to connect separated quantum nodes, which relies on the distribution of long-distance entanglement and active feedforward control of qubits between remote nodes. Such approaches can be utilized to construct nonlocal quantum gates, forming building blocks for distributed quantum computing and other novel quantum applications. However, these gates have only been realized within single nodes or between nodes separated by a few tens of meters, limiting the ability to harness computing resources in large-scale quantum networks. Here, we demonstrate nonlocal photonic quantum gates between two nodes spatially separated by 7.0 km using stationary qubits based on multiplexed quantum memories, flying qubits at telecom wavelengths, and active feedforward control based on field-deployed fibers. Furthermore, we illustrate quantum parallelism by implementing the Deutsch-Jozsa algorithm and the quantum phase estimation algorithm between the two remote nodes. These results represent a proof-of-principle demonstration of quantum gates over metropolitan-scale distances and lay the foundation for the construction of large-scale distributed quantum networks relying on existing fiber channels.

Skyrmion dynamics in attractive and repulsive local magnetic fields

The study of the behavior of magnetic skyrmions in local magnetic fields’ nanometric length-scale has gained increasing interest in recent years due to the theoretical proposal of magnetic skyrmion–superconducting vortex pairs as potential hosts for topologically protected bound states, which hold high promise for applications in quantum computing. From a magnetic interaction point-of-view, the key interest lies in understanding the skyrmion dynamics triggered by the magnetic energy landscape generated by the superconducting vortex. Here, we present a micromagnetic study of the dynamics of nanometric skyrmions inside a Gaussian magnetic field profile, which is used as a simplified version of the vortex magnetic flux. On the one hand, our calculations show that local non-linear magnetic fields can be very effective in controlling the dynamics of magnetic skyrmions; in particular, they offer the appealing possibility to manipulate skyrmions in a two dimensional space. On the other hand, they also show that the dynamics of a skyrmion in a local magnetic field can be manipulated via a uniform external magnetic field without any change in the magnetic field gradient. An analytical expression for the skyrmion velocity is given, and the corresponding microscopic dynamics are confirmed by the micromagnetic simulations. This work is expected to motivate more theoretical and experimental studies of the behavior of magnetic skyrmions in proximity to superconducting vortices.