Information Processing Architecture Research Articles

Control of magnon-magnon coupling in Ni80Fe20 nanocross arrays through system dimensions

Hybrid magnonics, founded on the coherent interplay between magnons and various quasiparticles, provides avenues for quantum transduction and the seamless transmission of coherent information. Magnon-magnon coupling, which exhibits remarkable coupling strength advantages over light-matter interactions, has emerged as a promising field. This research investigates magnon-magnon coupling in Ni80Fe20 nanocross arrays, with a focus on controlling the anticrossing phenomenon. Through a methodical adjustment of the nanocross arm length, it becomes possible to fine-tune the strength of coupling. The significance of the observed effect becomes evident when examining the power and phase profiles of distinct spin-wave (SW) modes in close proximity to and at a considerable distance from the anticrossing point. The research contributes to bridging the gap in hybrid quasi-particle interactions and offers insights into magnon-magnon coupling control. The findings open avenues for efficient magnon-based technologies and systems enabling efficient control of SW propagation characteristics and coupling, with implications for efficient quantum information processing architectures.

Fourier transform-based electronic logic information processing architecture design

Abstract To better develop the electronic information industry and make the electronic logic information processing architecture more widely used in production life. This paper uses the Fourier transform model based on the discrete Fourier transform and fast Fourier transform data simulation, for the electronic logic information architecture of single-signal and dual-signal signal spectrum analysis for simulation analysis. The electronic logic signal, whether it contains whole harmonics, non-harmonics, or attenuated DC components, can be accurately decomposed even when the system frequency deviates from 50 Hz, and the error value is guaranteed to be around 0.0016%. It is also pointed out that the presence of the attenuated DC component does not affect the results. The simulation results show that the design of electronic logic information processing architecture based on Fourier transform is better than other models under the sample data conditions. And the Fourier transform model can effectively guarantee the probability strength of logic information transfer and enhance the reliable line of results, which provides a research direction for the design of electronic logic information processing architecture.

Observation of Narrow Optical Homogeneous Linewidth and Long Nuclear Spin Lifetimes in a Prototypical [Eu(trensal)] Complex

Coherent light–matter interfaces are key elements for many quantum information processing architectures. Rare-earth ion (REI)-containing systems featuring narrow inter-configurational f–f transitions are suitable for creating such interfaces. Recently, narrow homogeneous linewidths (Γh) and long nuclear spin lifetimes (T1spin) associated with two Eu3+ complexes featuring the 5D0 → 7F0 f–f transition have been reported, elucidating that REI-molecule-based coherent light–matter interfaces can be obtained. In this study, we report a homogeneous linewidth of 2.8 MHz at 4.2 K, corresponding to an optical coherence lifetime (T2opt) of 114 ns (0.11 μs), associated with the 5D0 → 7F0 transition of the prototypical charge neutral Eu3+ complex─[Eu(trensal)]. Moreover, we have observed long nuclear spin lifetimes (T1spin) up to 460 ± 80 s at 4.2 K for the complex and efficient optical pumping of hyperfine levels of the 5D0 ground state. The results presented in this study indicate that narrow homogeneous linewidths and long nuclear spin lifetimes could be a generic property of molecular Eu3+ complexes featuring the 5D0 → 7F0 transition.

D2OP: A Fair Dual-Objective Weighted Scheduling Scheme in Internet of Everything

In times of the Internet of Everything (IoE), the power of the Internet is growing exponentially, followed by a surge in the number of network requests. The conflict between people’s high requirements for quality of experience (QoE) and limited computing resources are becoming increasingly prominent. Therefore, an appropriate offloading method is required to better ease this conflict. In this paper, a highly efficient scheduling architecture of information processing under the big data flow of the IoE is proposed to enhance the scheduling performance. First, we construct a dual-channel processing model to describe the entire data flow and node devices. Second, we carefully consider the choice of weighting method to better find a balance between dual objectives. Third, a dual-objective deep Q-network (DQN)-based offloading algorithm with principal component analysis weighting method (D2OP) is proposed to collaboratively minimize task response time and machine load in a more reasonable allocation. To verify the performance of the D2OP, a series of experiments are conducted from multiple angles. The experimental results demonstrate its better performance than the three comparison algorithms in reducing response time, load balance, and increasing task success ratio.

Universal conductance fluctuations in nanoscale topological insulator devices

Topological materials are promising candidates in fault-tolerant quantum information processing architectures, making it essential to understand the dephasing mechanisms in these materials. Here, we investigate gated, nanoscale mesas fabricated on thin films of cadmium arsenide (Cd3As2), a three-dimensional Dirac semimetal that can be tuned into different topological phases. We observe two independent types of conductance oscillations, one as a function of the applied magnetic field and the other as a function of the gate voltage. Varying the dimensions of the nanostructures allows the discrimination of a variety of scenarios for similar oscillations previously reported in the literature. We conclude that the conductance oscillations are not a signature of topological boundary states per se, but rather are universal conductance fluctuations. These results broadly inform future interpretations of electronic quantum interference in mesoscopic devices made from topological materials.

Learning Stochastic Graph Neural Networks With Constrained Variance

Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, which comes with no guarantee about deviations of particular output realizations around the optimal expectation. To overcome this issue, we propose a variance-constrained optimization problem for SGNNs, balancing the expected performance and the stochastic deviation. An alternating primal-dual learning procedure is undertaken that solves the problem by updating the SGNN parameters with gradient descent and the dual variable with gradient ascent. To characterize the explicit effect of the variance-constrained learning, we analyze theoretically the variance of the SGNN output and identify a trade-off between the stochastic robustness and the discrimination power. We further analyze the duality gap of the variance-constrained optimization problem and the converging behavior of the primal-dual learning procedure. The former indicates the optimality loss induced by the dual transformation and the latter characterizes the limiting error of the iterative algorithm, both of which guarantee the performance of the variance-constrained learning. Through numerical simulations, we corroborate our theoretical findings and observe a strong expected performance with a controllable variance.

Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions

An inflection point in the computing industry is occurring with the implementation of the Internet of Things and 5G communications, which has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. The purpose of edge computing is to provide computing, network control, and storage to the network edge to accommodate computationally intense and latency-critical applications at resource-limited endpoints. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server’s computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. This paper briefly summarizes the edge computing architecture for information and task processing. Meanwhile, the collaborative scheduling scenarios are examined. Resource scheduling techniques are then discussed and compared based on four collaboration modes. As part of our survey, we present a thorough overview of the various task offloading schemes proposed by researchers for edge computing. Additionally, according to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Finally, edge computing resource scheduling issues, challenges, and future directions have discussed.

Memristive Circuit Design of Brain-Like Emotional Learning and Generation.

In this work, a bionic memristive circuit with the functions of emotional learning and generation is proposed, which can perform brain-like emotional learning and generation based on various types of input information. The proposed circuit is designed based on the brain emotional learning theory in the limbic system, which mainly includes three layers of design: 1) the bottom layer is the design of the basic unit modules, such as neuron and synapse; 2) the middle layer is the design of the functional modules related to emotional learning in the limbic system, such as the amygdala, thalamus, and so on; and 3) the top layer is the design of the overall circuit, which is used to realize the function of the emotional generation. A 2-D emotional space composed of valence and arousal signals is adopted. According to the above bottom-up circuit design method, the valence and arousal signals can be generated, respectively, by designing corresponding emotional learning circuits, so as to form continuous emotions. The volatile and nonvolatile memristors are mainly used to mimic the functions of the neuron and synapse at the bottom layer of the circuit to achieve the core emotional learning function of the middle layer, thereby constructing a brain-like information processing architecture to realize the function of the emotional generation in the top layer. The simulation results in PSPICE show that the proposed circuit can learn and generate emotions like humans. If the proposed circuit is applied to a humanoid robot platform through further research, the robot may have the ability of personalized emotional interaction with humans, so that it can be effectively used in emotional companionship and other aspects.

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach.

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N= 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

Multi-Level Encoding and Decoding in a Scalable Photonic Tensor Processor With a Photonic General Matrix Multiply (GeMM) Compiler

The resurgence of artificial intelligence enabled by deep learning and high performance computing has seen a dramatic increase of demand in the accuracy of deep learning model which has come at the cost of computational complexity. The fundamental operations in deep learning models are matrix multiplications, and large scale matrix operations and data-centric tasks have experienced bottlenecks from current digital electronic hardware in terms of performance and scalability. Recent research on photonic processors have found solutions to enable applications in machine learning, neuromorphic computing and high performance computing using basic photonic processing elements on integrated silicon photonic platform. However, efficient and scalable photonic computing requires an information encoding/decoding scheme. Here, we propose a multi-level encoding and decoding scheme, and experimentally demonstrate it with a wavelength-multiplexed silicon photonic processor. We also discuss the scalability of our proposed scheme by introducing a photonic general matrix multiply compiler, and consider the effects of speed, bit precision, and noise. Our proposed scheme could be adapted to a variety of photonic information processing architectures for photonic neural networks, photonics tensor cores, and programmable photonic.

Narrow Optical Transitions in Erbium-Implanted Silicon Waveguides

The realization of a scalable architecture for quantum information processing is a major challenge for quantum science. A promising approach is based on emitters in nanostructures that are coupled by light. Here, we show that erbium dopants can be reproducibly integrated at well-defined lattice sites by implantation into pure silicon. We thus achieve a narrow inhomogeneous broadening, less than 1 GHz, strong optical transitions, and an outstanding optical coherence even at temperatures of 8 K, with an upper bound to the homogeneous linewidth of around 10 kHz. Our study thus introduces a promising materials platform for the implementation of on-chip quantum memories, microwave-to-optical conversion, and distributed quantum information processing.

Dense Incremental Metric-Semantic Mapping for Multiagent Systems via Sparse Gaussian Process Regression

In this article, we develop an online probabilistic metric-semantic mapping approach for mobile robot teams relying on streaming RGB-D observations. The generated maps contain full continuous distributional information about the geometric surfaces and semantic labels (e.g., chair, table, and wall). Our approach is based on online Gaussian process (GP) training and inference and avoids the complexity of GP classification by regressing a truncated signed distance function (TSDF) of the regions occupied by different semantic classes. Online regression is enabled through a sparse pseudo-point approximation of the GP posterior. To scale to large environments, we further consider spatial domain partitioning via a hierarchical tree structure with overlapping leaves. An extension to a multirobot setting is developed by having each robot execute its own online measurement update and then combine its posterior parameters via local weighted geometric averaging with those of its neighbors. This yields a distributed information processing architecture, in which the GP map estimates of all the robots converge to a common map of the environment while relying only on local one-hop communication. Our experiments demonstrate the effectiveness of the probabilistic metric-semantic mapping technique in 2-D and 3-D environments in both the single- and multirobot settings and in comparison to a deep TSDF neural network approach.

Mathematical study of neural feedback roles in small target motion detection.

Building an efficient and reliable small target motion detection visual system is challenging for artificial intelligence robotics because a small target only occupies few pixels and hardly displays visual features in images. Biological visual systems that have evolved over millions of years could be ideal templates for designing artificial visual systems. Insects benefit from a class of specialized neurons, called small target motion detectors (STMDs), which endow them with an excellent ability to detect small moving targets against a cluttered dynamic environment. Some bio-inspired models featured in feed-forward information processing architectures have been proposed to imitate the functions of the STMD neurons. However, feedback, a crucial mechanism for visual system regulation, has not been investigated deeply in the STMD-based neural circuits and its roles in small target motion detection remain unclear. In this paper, we propose a time-delay feedback STMD model for small target motion detection in complex backgrounds. The main contributions of this study are as follows. First, a feedback pathway is designed by transmitting information from output-layer neurons to lower-layer interneurons in the STMD pathway and the role of the feedback is analyzed from the view of mathematical analysis. Second, to estimate the feedback constant, the existence and uniqueness of solutions for nonlinear dynamical systems formed by feedback loop are analyzed via Schauder's fixed point theorem and contraction mapping theorem. Finally, an iterative algorithm is designed to solve the nonlinear problem and the performance of the proposed model is tested by experiments. Experimental results demonstrate that the feedback is able to weaken background false positives while maintaining a minor effect on small targets. It outperforms existing STMD-based models regarding the accuracy of fast-moving small target detection in visual clutter. The proposed feedback approach could inspire the relevant modeling of robust motion perception robotics visual systems.

Вычислительный эксперимент – обезразмеривание уравнений, вычислительная устойчивость и тестирование программ

From the stages of the computational experiment, the stage of non-dimensionalization of the initial equation (system of equations) of the problem is considered - the replacement of its variables by the product of the corresponding dimensionless quantities by their units of measurement with subsequent transformations. Such a transition from a physical model to a mathematical (dimensionless) one makes it possible to obtain software implementations for research. A critical evaluation of its complexity is carried out and possible errors in the results are evaluated. At the same time, new versions of software are formed. Object-oriented programming tools and version control systems (for example, git) allow you to create versions of software tools adapted to different conditions of their use and for different types of users. Parallelization of work on versions is carried out. At the same time, for further software implementation, the set-theoretic language of formulas with partially recursive functions is effective. To implement versions with large amounts of calculations and data, high-performance computing systems based on software and hardware acceleration, parallel information processing and cloud architectures are used. As a rule, a difference model of the problem and iterative methods for solving it are constructed for a program version. Computational stability conditions are usually stipulated in modern instructions for standard program libraries. For new algorithms, it is necessary to analyze the stability of difference schemes based on the refinement of their spectral properties and the use of functional analysis methods. For storage and subsequent application of the results of computational experiments, it is advisable to use modern databases. As a kind of computational experiment, testing of alpha and beta versions of programs and their releases is also considered.

AI Acceleration Enabled by Nanoelectronic Memristive Devices

Here we present an analysis of the current state in the field of development of hardware accelerators of artificial intelligence (AI). Despite the fairly good progress made over the past decades, this area is experiencing a number of significant difficulties in its development. The solution to this problem lies in the application of new approaches to the organization of computing, in particular, computing in memory enabled by nanoelectronic memristive devices. We provide an overview of state-of-art systems, as well as our own version of the experimental concept of AI accelerators based on metal-oxide memristive devices and the massively parallel architecture for information processing.

Speedup of entanglement generation in hybrid quantum systems through linear driving

The hybrid quantum system in which interactions between superconducting (SC) qubits and atomic qubits are mediated by an auxiliary cavity mode is a promising architecture for quantum information processing. However, compared to the strong coupling ${g}_{1}$ between the SC qubits and the cavity, the coupling ${g}_{2}$ between the atomic qubits and the cavity is often extremely weak, which limits the operation speed and suffers more from dissipations. Here we propose a built-in fault-tolerant geometric operation to speed up the generation of entanglement between SC qubits and atomic qubits only by linearly driving the qubits. The operation speed is proportional to $\sqrt{{g}_{1}{g}_{2}}$ instead of the weaker coupling ${g}_{2}$. Comparing with traditional double-swap method, the speedup can reach around 10 times and the fidelity keeps high under current experimental parameters. This speedup technique can be used in various boson-mediated quantum platforms even if parametric driving or modulation of the boson mode is impossible, enabling exploration of fast quantum information processing.

Cyber Attacks Prediction using Data Science

Cyber-attack, via cyberspace, targeting an enterprise's use of cyberspace for the purpose of disrupting, disabling, destroying, or maliciously controlling a computing environment/infrastructure; or destroying the integrity of the data or stealing controlled information. The state of the cyberspace portends uncertainty for the future Internet and its accelerated number of users. New paradigms add more concerns with big data collected through device sensors divulging large amounts of information, which can be used for targeted attacks. Though a plethora of extant approaches, models and algorithms have provided the basis for cyber-attack predictions, there is the need to consider new models and algorithms, which are based on data representations other than task-specific techniques. However, its non-linear information processing architecture can be adapted towards learning the different data representations of network traffic to classify type of network attack. In this paper, we model cyber-attack prediction as a classification problem, Networking sectors have to predict the type of Network attack from given dataset using machine learning techniques. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable identification, uni- variate analysis, bi-variate and multi-variate analysis, missing value treatments etc. A comparative study between machine learning algorithms had been carried out in order to determine which algorithm is the most accurate International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 06 Issue: 03 | March - 2022 ISSN: 2582-3930 © 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM11906 | Page 2 in predicting the type cyber Attacks. We classify four types of attacks are DOS Attack, R2L Attack, U2R Attack, Probe attack. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with entropy calculation, precision, Recall, F1 Score, Sensitivity, Specificity and Entropy. Keywords Cyber-attack, Cyberspace, DOS Attack, R2L Attack, U2R Attack, Probe attack

A Three-Level Distributed Architecture for the Real-Time Monitoring of Modern Power Systems

To monitor network operation in real-time, power system operators have developed wide-area monitoring systems (WAMS). However, the centralized communication and information processing architecture of WAMS cannot be extended easily to distribution networks. In this aspect, a three-level distributed network monitoring architecture is proposed in this paper, concerning the dynamic analysis of transmission, primary and secondary distribution networks by exploiting measurements of ambient data and transient responses. In the proposed architecture, operators are responsible for the operation and analysis of their own grid but also can share an overview of the system performance to facilitate their operational coordination. Different online and offline applications are supported within the architecture, including small-signal, transient and frequency stability analysis as well as dynamic equivalencing and real-time inertia estimation. Measurement-based algorithms and models are proposed for each case. Finally, the performance of the developed algorithms has been tested by using a combined transmission and distribution power system model.

Digital Design of Smart Museum Based on Artificial Intelligence

Today, as the soft power of culture is becoming more and more important, it is very important to pay attention to the learning and dissemination of culture. As the carrier of this process, the use of advanced technology to improve the museum is of great significance. This paper studies the digital design of smart museum based on artificial intelligence in order to explore the application of smart museum in artificial intelligence, analyze the spatial design of smart museum by using digital technology, explore a feasible method to give full play to the function of smart museum, and put forward some suggestions on the spatial design of smart museum. The design of the smart museum is no longer restricted by time and space and uses digital technology to double use virtual things and dynamic space. Through the detailed analysis of the application of artificial intelligence and digitization in the spatial design of the smart museum, combined with the information decision tree algorithm and data heterogeneous network algorithm, this study constructs the model of the information processing architecture of smart museum and the requirements of digital museum and makes a decision-making analysis of the comparison results of existing data. It includes the digital design of smart museum display technology, display effect, and other display-related contents. Analyzing the impact of smart museum on the object can provide data support for the feasibility of digital space design of smart museum based on artificial intelligence. The results of regression data processing show that the spatial visual sense of digital design wisdom museum is very strong, reaching the level of 5.0, and the picture aesthetic effect is up to 4.8.

Secure Integration of IoT and Cloud Computing

Mobility Technique Is a fairly latest invention that relates to an information storage and processing architecture that is separate from the wireless phone. The IoT is a brand-new concept. The Internet of Things (IoT) is a comparatively recent networking technologies that is increasing popularity swiftly. IoT is more particularly linked to wireless telecommunications. The