The paper explores the implementation of quantum gates using python programming, aiming to simulate the functionality of quantum computers within classical systems. Leveraging the outstanding capability of python, qua...
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ISBN:
(纸本)9798350372113;9798350372106
The paper explores the implementation of quantum gates using python programming, aiming to simulate the functionality of quantum computers within classical systems. Leveraging the outstanding capability of python, quantum gates have been meticulously programmed and executed to investigate its behaviour in comparison to traditional quantum computing frameworks. The study involved an in-depth analysis of the programmed gates' performance, evaluating their ability to produce results consistent with those expected in quantum computing environments.
Fundamental in numerical computing, mean square (MS) and mean squared error (MSE) calculations play a vital role, especially in artificial intelligence (AI) and signal processing applications. This paper introduces a ...
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ISBN:
(数字)9798350349597
ISBN:
(纸本)9798350349603;9798350349597
Fundamental in numerical computing, mean square (MS) and mean squared error (MSE) calculations play a vital role, especially in artificial intelligence (AI) and signal processing applications. This paper introduces a versatile, low-complexity, and adaptable approach to implement MS and MSE calculations. The central concept involves employing in-memory computing (IMC) technique and executing computations through a set of memristor devices. Specifically, the necessary multiplications and additions leverage the inherent properties of memristor devices, adhering to Ohm's law and Kirchhoff's current law. This innovative method enhances flexibility by programming memristors and emphasizes processing efficiency by reducing computational complexity and latency, surpassing traditional implementation methods.
The proceedings contain 57 papers. The special focus in this conference is on Ubiquitous computing and intelligentinformationsystems. The topics include: Survey on Handwritten Characters Recognition in Deep Learning...
ISBN:
(纸本)9789811925405
The proceedings contain 57 papers. The special focus in this conference is on Ubiquitous computing and intelligentinformationsystems. The topics include: Survey on Handwritten Characters Recognition in Deep Learning;a Survey on Wild Creatures Alert System to Protect Agriculture Lands Domestic Creatures and People;a Study on Surveillance System Using Deep Learning Methods;IRHA: An intelligent RSSI Based Home Automation System;a Review Paper on Machine Learning Techniques and Its Applications in Health Care Sector;Enhanced Mask-RCNN for Ship Detection and Segmentation;data Scientist Job Change Prediction Using Machine Learning Classification Techniques;Error Correction Scheme with Decimal Matrix Code for SRAM Emulation TCAMs;knowledge Discovery in Web Usage Patterns Using Pageviews and Data Mining Association Rule;a Six-Point Based Approach for Enhanced Broadcasting Using Selective Forwarding Mechanism in Mobile Ad Hoc Networks;video Anomaly Detection Using Optimization Based Deep Learning;a Fusional Cubic-Sine Map Model for Secure Medical Image Transmission;Innovative Technologies Developed for Autonomous Marine Vehicles by ENDURUNS Project;machine Learning Approaches to Predict Breast Cancer: Bangladesh Perspective;A Comparative Review on Image Analysis with Machine Learning for Extended Reality (XR) Applications;SWOT Analysis of Behavioural Recognition Through Variable Modalities;e-Mixup and Siamese Networks for Musical Key Estimation;microarray Data Classification Using Feature Selection and Regularized Methods with Sampling Methods;visual Place Recognition Using Region of Interest Extraction with Deep Learning Based Approach;electronic Mobility Aid for Detection of Roadside Tree Trunks and Street-Light Poles;crop Price Prediction Using Machine Learning Naive Bayes Algorithms;enneaontology: A Proposed Enneagram Ontology.
The complexity of financial systems and the rapid increase in data volume are making the use of intelligentcomputing and trustworthy machine learning more important in finance. This paper discusses how intelligent co...
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ISBN:
(纸本)9798350377859;9798350377842
The complexity of financial systems and the rapid increase in data volume are making the use of intelligentcomputing and trustworthy machine learning more important in finance. This paper discusses how intelligentcomputing can be applied within complex financial systems and takes a deeper look at the theory behind trustworthy machine learning and how it is used in finance. By combining the structure of complex networks with the computing power of machine learning, the paper also explores the inner workings of large neural networks and considers how to apply the theory of dynamic systems to the tuning of these networks, to improve intelligentcomputing in complex financial systems. Experiments based on the "scientific intelligence + machine conjecture" approach were carried out for risk assessment and market forecasting. The results show that these technologies can improve how financial institutions manage risk, help investors get more reliable information about market trends, and meet the transparency needed for regulatory compliance. The use of intelligentcomputing and trustworthy machine learning in complex financial systems points to a future with lots of potential for innovation and new opportunities.
The exponential growth of IoT applications due to user demands necessitates the emergence of reliable fog/edge computingsystems. Because of the wide-open area of these systems, the tendency for failure in communicati...
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ISBN:
(纸本)9798350372113;9798350372106
The exponential growth of IoT applications due to user demands necessitates the emergence of reliable fog/edge computingsystems. Because of the wide-open area of these systems, the tendency for failure in communication channels and computing nodes (resources) is unpredictable, especially in real-time IoT applications. Several studies have aimed at tackling fault and failure issues by employing a variety of resource management strategies. Most studies use heuristics or conventional scheduling approaches that leverage the use of reinforcement learning. The element of reinforcement learning aims to support better governance when resource faults and failures occur. This paper provides an analysis of the implementation of reinforcement learning (RL) into fault-tolerant scheduling in fog/edge computingsystems. The efficacy of fault-tolerant scheduling is claimed to be improved through RL with regard to system performance. We grouped the discussion based on three themes: resource failures, reinforcement learning (RL) in general, and RL scheduling for fault tolerance. The discussion could further derive solutions for overcoming the challenges of resource failures in fog/edge systems.
Prominence is one of the most important measurements in topography and mountaineering. This paper describes an efficient, almost linear time algorithm for computing mountain prominence for all peaks on Earth using dig...
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ISBN:
(纸本)9798350368130
Prominence is one of the most important measurements in topography and mountaineering. This paper describes an efficient, almost linear time algorithm for computing mountain prominence for all peaks on Earth using digital elevation models (DEMs). It builds on top of a classic algorithm and leverages the observation that only a few peaks have their prominence determined by a relatively distant other mountain. Thus, the classic algorithm can be adapted to memorize and use less information without the loss of correctness. The algorithm is demonstrated using 3 arcsecond real-life data from SRTM datasets. Its importance is underscored by the increasing accuracy of Earth mapping methods and the corresponding growth in the amount of data that must be processed to compute prominence.
The urgent need for industrial efficient solutions allowing to reduce the environmental footprint of modern man-made systems is nowadays acknowledged by all and becomes a central issue. This reality is all the more tr...
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ISBN:
(纸本)9798350373981;9798350373974
The urgent need for industrial efficient solutions allowing to reduce the environmental footprint of modern man-made systems is nowadays acknowledged by all and becomes a central issue. This reality is all the more true for cyber-physical systems where the integrations of computation, networking, and physical processes are intensively realized. These intelligentsystems which combine several computing resources interacting with the physical environment using sensors and actuators are known to be energy-intensive. So, the management of the energy becomes one of the major concerns driven by the increasing number of connected systems. This paper addresses the challenge of estimating least-cost firing sequences within P-Time Labeled Petri Net systems, incorporating both energy considerations and temporal constraints into the modeling framework. By extending the existing models with a dynamic programming algorithm, the proposed method calculates optimal sequences that minimize energy consumption for a given set of actions over time. An industrial example is presented to illustrate the practical application of the approach, demonstrating the potential for significant energy savings and efficiency improvements in industrial systems.
In this paper, a real-time energy-optimal strategy exploiting preview information resulting from connectivity and autonomous vehicle (CAV) technology is verified by experimental analyses linked with on-board computing...
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ISBN:
(纸本)9798350399462
In this paper, a real-time energy-optimal strategy exploiting preview information resulting from connectivity and autonomous vehicle (CAV) technology is verified by experimental analyses linked with on-board computing methods on a real vehicle-in-the-loop testbed with virtual road and traffic light system. An energy-optimal deceleration planning/following system (EDPS) as a service-oriented technology for electrified vehicles utilizing preview information is applied to a microcontroller, where the data access route and location on the given system architecture are optimized to shorten computing time. Also, two types of multicore strategies are comparatively analyzed to efficiently operate computing resources of the embedded controller as well as to distribute computing loads, and the strategy comparisons indicate that a function-level task partition considering target cores in advance can practically reduce computing loads. In the vehicle-in-the-loop simulations (VILS) with a realistic driving on the virtual road and CAV technology-based information, the embedded EDPS planning results illustrate that the energy-optimal speed profiles are properly computed on a commercial automotive microcontroller while stably processing real-time data input/output and optimal planning within the given time constraints.
With the emergence of information-Centric Networking as a potential paradigm shift in the computer networking field, the coupling between VANETS and Named-Data Networking, known as VNDN, has gained momentum as a promi...
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ISBN:
(纸本)9798350369458;9798350369441
With the emergence of information-Centric Networking as a potential paradigm shift in the computer networking field, the coupling between VANETS and Named-Data Networking, known as VNDN, has gained momentum as a promising communication standard to foster the deployment of inter-vehicular communications in the real world, calling the attention of the research community. This work presents a concise VNDN review focusing on the background concepts. We also analyze the current status and open challenges, including the prominent data naming and caching approaches and the most efficient Interest and Data message forwarding and delivery strategies.
More and more portable intelligent devices are connected to the Internet in recent years. A way to effectively use the isolated cyber data without involving privacy and realize the cyber intrusion anomaly detection on...
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ISBN:
(纸本)9798350395716;9798350395709
More and more portable intelligent devices are connected to the Internet in recent years. A way to effectively use the isolated cyber data without involving privacy and realize the cyber intrusion anomaly detection on the portable intelligent devices with relatively limited hardware storage resources and computing power is worth exploring. In this paper, we propose a framework of federated anomaly detection, which enables the device effectively detect the anomaly by sharing the parameters of the federated model in a fully distributed fashion. We formulate the model training problem as a distributed robust optimization problem and subsequently devise an efficient algorithm for it. Experimental studies have also been carried out to reveal the superior performance of the proposed framework and underscore the significant benefits of federated anomaly detection.
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