Computer Performance Research Articles

AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture

This study discussion point of this paper is to make an in-depth analysis of the development impact of the Internet of Things combined with edge computing and artificial intelligence. In the analysis process, the importance and criticality of data processing and decision making of edge computing as well as the challenges faced should be elaborated respectively. With the rapid popularization and development of Internet of Things devices, edge computing has brought more innovative solutions for different application scenarios such as intelligent furniture industrialization, automatic driving and intelligent transportation by reducing the delay of processing data and improving the characteristics of security data, film and television. Resource and energy efficiency have certain limitations, so it is necessary to combine artificial intelligence to enhance edge computing devices, hardware accelerators, and its utility and federated learning technologies, which can effectively improve the performance and scalability of edge computing and promote the development of more self-service network systems for smart devices. The core of this study is how to promote the Internet through AI-driven edge computing to further develop and provide insights for research priorities and suggest related future research directions.

Quantum computer error structure probed by quantum error correction syndrome measurements

With quantum devices rapidly approaching qualities and scales needed for fault tolerance, the validity of simplified error models underpinning the study of quantum error correction needs to be experimentally evaluated. In this work, we have assessed the performance of IBM superconducting quantum computer devices implementing heavy-hexagon code syndrome measurements with circuit sizes up to 23 qubits, against the error assumptions underpinning code threshold calculations. Circuit operator change rate statistics in the presence of depolarizing and biased noise were modeled using analytic functions of error model parameters. Data from 16 repeated syndrome measurement cycles were found to be inconsistent with a uniform depolarizing noise model, with slightly improved fits found with biased and inhomogeneous noise models. Spatial-temporal correlations investigated via Z stabilizer measurements revealed significant temporal correlation in detection events. These results highlight the nontrivial structure that may be present in the noise of quantum error correction circuits, revealed by operator measurement statistics, and support the development of noise-tailored codes and decoders to adapt. Published by the American Physical Society 2024

Automatic detection and counting of wheat spike based on DMseg-Count

The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike. In order to solve the above problems and further improve the accuracy of wheat spike counting, an improved wheat spike counting model DMseg-Count was proposed by enhancing local contextual supervision information based on existing target object counting model DM-Count. Firstly, wheat spike local segmentation branch was introduced to improve the network architecture of DM-Count, so as to extract the local contextual supervision information of wheat spike. Secondly, an element-by-element point multiplication mechanism was designed to fuse global and local contextual supervision information of wheat spike. Finally, the total loss function was constructed to optimize the model. The test results showed that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed DMseg-Count model were 5.79 and 7.54, respectively, which were 9.76 and 10.91 higher than the standard distribution matching for crowd counting (DM-Count) model. Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. In summary, the proposed DMseg-Count model can effectively detect wheat spike and has good counting performance, which provides a new method for automatic counting of wheat spike and yield prediction in complex field environments.

Predicting the generalization of computer aided detection (CADe) models for colonoscopy

Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. We show that a "Masked Siamese Network" (MSN), trained to predict masked out regions of polyp images without labels, can predict the performance of Computer Aided Detection (CADe) of polyps on colonoscopies, without labels. This holds on Japanese colonoscopies even when MSN is only trained on Israeli colonoscopies, which differ in scoping hardware, endoscope software, screening guidelines, bowel preparation, patient demographics, and the use of techniques such as narrow-band imaging (NBI) and chromoendoscopy (CE). Since our technique uses neither colonoscopy-specific information nor labels, it has the potential to apply to more medical imaging domains.

Comprehensive Analysis of Cloud Computing Performance Factors: Investigating the Impact of Response Time, Load Balancing and Service Broker Policies on Cloud Service Efficiency Using CloudSim Simulation

Cloud performance refers to the efficiency and effectiveness with which a cloud system operates delivering hosted services over the internet. As cloud computing continues to offer flexibility, scalability and computational power monitoring and improving cloud performance is essential. Performance optimization is influenced by factors such as load balancing and service broker policies which impact system response times and overall user experience. This paper provides an in-depth review of key publications and real-time cloud performance tools identifying critical performance factors that affect cloud efficiency. Notably, response time emerged as a fundamental metric for cloud service quality. Using CloudSim simulation we examine cloud performance evaluation criteria and experimentally assess the impact of response time dependencies on broker policies, load balancing techniques and data center distribution. This study offers a framework for understanding cloud performance evaluation and highlights strategies to enhance user experience in diverse cloud environments.

Influence of the stoichiometric ratio of barrier layer alumina on the transport properties of Josephson junctions

In the field of quantum computing, the Josephson junction is one of the main components for generating superconducting qubits. The material defects present in the junction affect the stability and reproducibility of qubits, which consequently limits the performance of quantum computing. In this paper, we conduct a detailed study of the crystal structure of alumina at different stoichiometric ratios and its electrical properties using density-functional theory (DFT). Additionally, we simulate the amorphous structure at various stoichiometric ratios using molecular dynamics methods. Our results show that a decrease in the oxygen content in the crystalline alumina material leads to a linear reduction in the bandgap. We then construct Josephson junction models with different stoichiometric ratios of the barrier layer based on our study, and calculate the equilibrium conductance for each model using the Non-equilibrium Green’s Function (NEGF) method. Our results indicate that the structure of the barrier layer significantly affects the electrical properties of the Josephson junction. Moreover, the stoichiometric ratio of alumina is a critical parameter that results in exponential variations in the equilibrium conductance. In summary, we seek to elucidate the role that variations in the stoichiometric ratio of the oxide play in the transport properties of the junction. Our findings provide a theoretical basis for improving the stability and performance of superconducting qubits.

Analog Implementation of a Spiking System for Performing Arithmetic Logic Operations on Mixed‐Signal Neuromorphic Processors

In recent years, physical limitations in the integration of transistors in computers have forced the search for low‐computational‐power alternatives in hardware design. Although doubts may arise regarding the limit of the relationship between performance and power consumption in computers, these disappear when considering the brain, which is one of the most efficient computing systems. In this way, bioinspired applications try to benefit from the low‐power consumption present in the biological nervous system. Previous work has shown the feasibility of implementing spiking neural networks that operate in a Boolean manner on digital platforms, such as SpiNNaker, using basic logic gates and a spiking memory, which suggests the potential for constructing a low‐power consumption spiking computer. This work takes a first step in the implementation of a spiking central processing unit by developing an arithmetic logic unit, which is an essential block for instruction execution, demonstrating its correct operation on Dynap‐SE1. The results confirm the feasibility of using this Boolean approach on this platform, despite certain limitations in the number of inputs and operating frequencies of the blocks, and pave the way for the construction of a spiking computer.

Использование цифровых моделей местности высокого пространственного разрешения для выделения водотока

Global digital surface (DSM) and elevation (DEM) models of medium spatial resolution (30 m) are traditionally used for extraction of vector hydrography for large areas. The article considers the possibility of waterline delineation by the tools of geographic information systems in relation to the regional digital surface model ArcticDEM of high (2 and 10 m) and medium (32 m) spatial resolution to assess the correctness and feasibility of this approach in hydrographic modeling. Since the algorithm of waterline delineation depends on the relief of the adjacent territories, three objects were selected for the study—the Northern Dvina River, the Moyero River and the Moma River, flowing in different geographical conditions. In order to validate the results of waterline delineation using DSM and DEM, their reference vector objects were created. Computer performance was considered in terms of processing high spatial resolution data. The revealed dependence of the calculation time of auxiliary layers on model’s spatial resolution makes it possible to estimate the expected load on a particular personal computer when conducting similar studies. It has been established that the usage of high spatial resolution models for waterline delineation cannot guarantee an increase in the correctness of the resulting vector hydrography. On the example of the part of the Northern Dvina River, longitudinal profiles of the terrain were constructed using DSM and DEM, which allow us to judge the quality of the initial data before using the waterline delineation algorithm. The longitudinal profiles can be used to identify model artifacts in places of unusually high elevation differences. These same areas will be the most incorrect in the resulting vector hydrographic objects, which means that they should be given more attention when performing automated delineation and validating its results.

Future projections for mammalian whole-brain simulations based on technological trends in related fields

Large-scale brain simulation allows us to understand the interaction of vast numbers of neurons having nonlinear dynamics to help understand the information processing mechanisms in the brain. The scale of brain simulations continues to rise as computer performance improves exponentially. However, a simulation of the human whole brain has not yet been achieved as of 2024 due to insufficient computational performance and brain measurement data. This paper examines technological trends in supercomputers, cell type classification, connectomics, and large-scale activity measurements relevant to whole-brain simulation. Based on these trends, we attempt to predict the feasible timeframe for mammalian whole-brain simulation. Our estimates suggest that mouse whole-brain simulation at the cellular level could be realized around 2034, marmoset around 2044, and human likely later than 2044.

Application of Reservoir Computers for Chaos Synchronization and Cracking Chaos-Based Cryptography in Fermionic 2D Thirring and 4D Gursey Models with Spinor Fields

Reservoir computers have recently become one of the most widely exploited model-free approaches to chaos synchronization due to their fast convergence speed and simple yet effective learning rules. In this study, reservoir computing has been utilized to emulate the dynamics of chaotic Thirring and Gursey systems. In the supervised learning of the reservoir, one-step ahead states of the dynamical systems have been employed as the teaching signals. After the training phase, the reservoir was first run autonomously and then weakly driven by chaotic systems. It has been shown that the trained reservoir computers can exhibit the same characteristics as the attractors of the learned chaotic systems, which enable their use in synchronization tasks of chaotic systems with possible application in cracking chaos-based cryptography. To investigate the effect of noise on the performance of reservoir computers, noise signals with different colors and amplitudes have been included in the transmitted signals. The obtained results indicate that the proposed scheme can provide lower error metrics for both models.

Effects of AlOx Sub‐Oxide Layer on Conductance Training of Passive Memristor for Neuromorphic Computing

AbstractMemristors are recognized as crucial devices for the hardware implementation of neuromorphic computing. The conductance training process of memristors has a direct impact on the performance of neuromorphic computing. However, memristor breakdown and conductance decay still hinder the precise training process of neural networks based on passive memristor. Here, AlOx/LiNbO3 (LN) memristors are designed by inserting a AlOx sub‐oxide layer between the single‐crystalline LN thin film with oxygen vacancies (OVs) and Pt layer. Under the same training conditions, lower conductance and self‐compliance current effects are observed in AlOx/LN memristor. Slight spontaneous decay of conductance is achieved after the removal of the external stimulation. To explore the effects of AlOx sub‐oxide layer on the prevention of device breakdown and suppression of conductance decay, the memristive mechanism of devices with and without AlOx layer is revealed via time‐of‐flight secondary ion mass spectrometer (ToF‐SIMS). It is reasonable to believe that the AlOx inserting layer in memristors can serve as a self‐compliance current layer to inhibit device breakdown and provide the OVs reservoir to suppress conductance decay. These results offer new possibilities and theoretical grounds for achieving more reliable and precise conductance training of passive memristors.

Infidelity Analysis of Digital Counter-Diabatic Driving in Simple Two-Qubit System.

Digitized counter-diabatic (CD) optimization algorithms have been proposed and extensively studied to enhance performance in quantum computing by accelerating adiabatic processes while minimizing energy transitions. While adding approximate counter-diabatic terms can initially introduce adiabatic errors that decrease over time, Trotter errors from decomposition approximation persist. On the other hand, increasing the high-order nested commutators for CD terms may improve adiabatic errors but could also introduce additional Trotter errors. In this article, we examine the two-qubit model to explore the interplay between approximate CD, adiabatic errors, Trotter errors, coefficients, and commutators. Through these analyses, we aim to gain insights into optimizing these factors for better fidelity, a shallower circuit depth, and a reduced gate number in near-term gate-based quantum computing.

Non-Periodic Quantized Model Predictive Control Method for Underwater Dynamic Docking

This study proposed an event-triggered quantized model predictive control (ETQMPC) method for the dynamic docking of unmanned underwater vehicles (UUVs) and human-occupied vehicles (HOVs). The proposed strategy employed a non-periodic control approach that initiated the non-linear model predictive control (NMPC) optimization and state sampling based on tracking errors and deviations from the predicted optimal state, thereby enhancing computing performance and system efficiency without compromising the control quality. To further conserve communication resources and improve information transfer efficiency, a quantitative feedback mechanism was employed for sampling and state quantification. The simulation experiments were performed to verify the effectiveness of the method, demonstrating excellent docking trajectory tracking performance, robustness against bounded current interference, and significant reductions in computational and communication burdens. The experimental results demonstrated that the method outperformed in the docking trajectory tracking control performance significantly improved the computational and communication performance, and comprehensively improved the system efficiency.

Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics.

In this work, we aim to unveil the general correlations between the performance of a physical reservoir computing (RC) system and the inherent nonlinear dynamics of the adopted device. Taking the metal-ferroelectric-metal (MFM) capacitor, one of the most popular candidate devices for compute-in-memory (CIM) technology, as the computational platform, we construct a nonlinear dynamical model of polarization in the ferroelectric layer. We then design the physical RC utilizing a single and/or an array of MFM capacitors by analyzing the model's stability and feasible dynamical cases. Subsequently, both the initial task and benchmark are numerically conducted to verify the designed RC's superiority. It is proven that by selecting an appropriate dynamical case, the RC can achieve a recognition rate as high as 96.13%, surpassing the results reported in previous work. Finally, we discuss how these key parameters play their role in the RC's performance from the perspective of affecting the system's transient responses, nonlinearity, and short-fading memory. This work paves the foundation for designing highly efficient reservoir computing based on MFM capacitors as well as other memristive devices such as memristors, tunneling diodes, etc.

Ion-chain sympathetic cooling and gate dynamics

Sympathetic cooling is a technique often employed to mitigate motional heating in trapped-ion quantum computers. However, choosing system parameters such as number of coolants and cooling duty cycle for optimal gate performance requires evaluating trade-offs between motional errors and other slower errors such as qubit dephasing. The optimal parameters depend on cooling power, heating rate, and ion spacing in a particular system. In this study, we aim to analyze best practices for sympathetic cooling of long chains of trapped ions using analytical and computational methods. We use a case study to show that optimal cooling performance is achieved when coolants are placed at the center of the chain and provide a perturbative upper bound on the cooling limit of a mode given a particular set of cooling parameters. In addition, using computational tools, we analyze the trade-off between the number of coolant ions in a chain and the center-of-mass mode heating rate. We also show that cooling as often as possible when running a circuit is optimal when the qubit coherence time is otherwise long. These results provide a roadmap for how to choose sympathetic cooling parameters to maximize circuit performance in trapped-ion quantum computers using long chains of ions. Published by the American Physical Society 2024

Development of intelligent suite for malaria pathogen detection in microscopy images

The identification of malaria infection using microscope images of blood smears is considered as a ‘gold standard’. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, it is desirable to automate the repetitive task of pathogen detection in the blood samples received as microscope images. This study provides an easy to use and deploy method for implementing a malaria pathogen detection software- the Intelligent Suite. The Intelligent Suite features a graphical user interface (GUI) implemented using ‘cvui’ library to interact with the OpenVINO’s inference engine for model optimisation and deployment across several inference devices. The intelligent Suite uses a custom YOLO-mp-3l model trained on Darknet framework for detection of malaria pathogen in thick smear microscope images. Moreover, the Intelligent Suite provides user interface for inference device/mode selection, alter model parameters, and generate detection reports along with the model performance metrics. The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-play Neural Compute Stick (NCS2) and performance reported.

AI for Cloud Cost Management: Predictive and Prescriptive Analytics

With reference to the previous information and literature review, the purpose of this research paper is to evaluate the impact of multithreading concerning computer resource utilization and performance by undertaking and comparing various crucial multithreading techniques, including work stealing, fork join, and the use of the thread pool control. In an experimental context, benchmark applications that can be characteristic of several domains were evaluated in a controlled multicore computing platform. Time, CPU occupation, memory usage and throughputs were determined systematically and compared according to the various workloads. The results highlight the superiority of the work-steing algorithm in keeping the execution time and CPU usage low compared to the rest as a sign of its efficiency in managing work loads. Further, the memory usage and the throughput statistics showed the degree of inefficiency of each algorithm and performance penalty. Interviews with developers elaborated on more practical issues of multithreading as the generally rigid nature of the problems suggested that optimal solutions called for adaptive measures in practice. Based on the results achieved in this research, developers and researchers can increase their knowledge about available multithreading algorithms and make suggestions to choose and apply them with high efficiency and low resource consumption.

Optimizing Cloud Computing Performance by Integrating the Novel PSBR Service Broker Policy and Load Balancing Algorithms

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

Cloud Computing Performance Evaluation Model for Open and Distance Learners in Nigeria

The increasing need for fast, secure and dynamic processing of information has necessitated the wide scale adoption of cloud computing. The educational sector especially the open and distance learning institutions are not left out of the need for distributed technology. This study presented a model that provided insight into the pre and post usage adoption variables associated with cloud computing adoption and performance in open and distance learning (ODL) setting. Partial Least Square (PLS) was adapted to test seven hypotheses on the causal relationship between the variables. Five out of the seven hypotheses were supported. The pre-usage and satisfaction oriented constructs showed more significant contributions in determining performance impact than the task technology fit oriented constructs. The study also provided insights on user preferences for decision making by educational institutions, service providers, business owners and government. Keywords: Cloud Computing, TUSPEM, Performance, Utilization, Cost, Ease Of Use

Personalized cancer treatment; How AI is shaping precision medicine

The application of artificial intelligence has demonstrated promising outcomes in certain areas of oncology, such as the screening, detection, diagnosis, treatment, and prediction of prognosis for tumors. New learning methods, such as the hybrid learning method, will continue to emerge as artificial intelligence (AI) continues to advance, computer performance continues to improve, and the explosion of various data continues to make it possible for new learning methods to emerge. These new learning methods will further improve the overall performance of the model, including efficient data analysis and accurate prediction. Both machine learning and deep learning have recently produced a model that is capable of analyzing a variety of data sets, which will also boost the prospects of PM. As a conclusion, artificial intelligence-assisted prenatal care has the potential to aid in the early detection, diagnosis, and treatment of cancer. Additionally, it can provide assistance in the selection of the most appropriate treatment plan, so enhancing the prognosis of patients and the outcomes of their treatment.