People can act out fake social exchanges in the Metaverse, which is an online community. Security has become a problem in the Metaverse because there are so many online transactions and digital assets. In this thesis,...
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The development of data processing technologies, microelectronics and sensor systems allows for high-precision multiparametric analysis of biosignals in real time. The paper considers the problem of automating medical...
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ISBN:
(数字)9798331510886
ISBN:
(纸本)9798331510893
The development of data processing technologies, microelectronics and sensor systems allows for high-precision multiparametric analysis of biosignals in real time. The paper considers the problem of automating medical processes to reduce the influence of the human factor and increase the accuracy of diagnostics. An improved method of multiparametric analysis of biosignals is proposed for long-term monitoring of the state of the cardiovascular system using modern sensor devices, data processing algorithms and artificial intelligence technologies. The research is aimed at improving the methods of collecting, transmitting and analyzing biosignals, which contributes to the creation of personalized medical devices and effective prediction of cardiovascular pathologies. The issues of classification of devices and biosignals, as well as their mathematical modeling to increase the accuracy of diagnostics, are considered.
Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains c...
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ISBN:
(数字)9798331542375
ISBN:
(纸本)9798331542382
Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages.
This paper presents a novel dual-functional hybrid Reconfigurable Intelligent Surface (RIS) for simultaneous sensing and reconfigurable reflections. We design a novel hybrid unit cell featuring dual elements, which sh...
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ISBN:
(数字)9788831299107
ISBN:
(纸本)9798350366327
This paper presents a novel dual-functional hybrid Reconfigurable Intelligent Surface (RIS) for simultaneous sensing and reconfigurable reflections. We design a novel hybrid unit cell featuring dual elements, which share the same phase center, to support both intended functionalities, with the antenna being miniaturized via a high dielectric material approach. The hybrid unit cell has a size of one eighth of the wavelength forming the foundation of an innovative metasurface that incorporates a sub-wavelength reflecting array of split-ring unit cells integrated with a load-tuning matrix. In particular, two interleaved sensing arrays of half-wavelength spacing, orthogonal polarization, and quarter-wavelength offset are embedded within the proposed dual-functional RIS, each tasked to sense the channel parameters towards one of the end communication nodes wishing to profit from the surface's reconfigurable reflections. Our full-wave simulations, indicatively centered around the frequency of 5.5 GHz, showcase the promising performance of both designed hybrid unit cells and reflective split-ring ones.
The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges....
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This paper presents the Pinch Sensor, an elastic input device to sense the fine motion and pinch force of the index finger and thumb - the two most used digits of human hands for in-hand object manipulation skills. In...
This paper presents the Pinch Sensor, an elastic input device to sense the fine motion and pinch force of the index finger and thumb - the two most used digits of human hands for in-hand object manipulation skills. In addition to open and close, the device would allow a user to control a robotic or simulated two-finger hand to reorient an object in three different ways and their combinations. A unique design of elastic sensing provides the users a high degree of perception resolution, as well as the sensation of holding an object with a certain level of stiffness between the index finger and thumb. These characteristics help the users to fine control the pinch force while carrying out manipulation skills. The design features a small size that allows it to be integrated to a handheld controller. Commonly available off-the-shelf components for consumer electronics are used to achieve affordability and reliability.
People with visual impairments perceive their environment non-visually and often use AI-powered assistive tools to obtain textual descriptions of visual information. Recent large vision-language model-based AI-powered...
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Security and privacy remain as a significant challenge in the distributed Internet of Things (IoT) framework. IoT has already demonstrated that it can significantly impact our daily lives. The internet sends and recei...
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The expansion of the internet of things (IoT) and Big Data are two factors that have contributed to the rise in popularity of smart cities. The ability to anticipate the quality of the air in an area with precision an...
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The expansion of the internet of things (IoT) and Big Data are two factors that have contributed to the rise in popularity of smart cities. The ability to anticipate the quality of the air in an area with precision and efficiency is one of the fundamental building blocks of a smart city. The amount of polluted air found in smart cities throughout the world has been gradually growing. Because of this, there has been a rise in the concentration of several air pollutants in the environment, including particulate matter (PM 10), sulphur dioxide (SO2), and PM 2.5, amongst others. Because of the possibility of uncontrollable repercussions, such as an increase in the severity of asthma and cardiovascular disease, this situation poses a risk to the country and to the people who live in it. Heavy industry and vehicle exhaust have been major contributors to the growth of air pollution in smart cities such as New Delhi, Bombay, Chandigarh, and Bengaluru in India. The purpose of this investigation is to compare and contrast the efficiency of a variety of machine learning methods in order to assess the precision of the air quality index (AQI) projection of PM 2.5 in Chandigarh, India. Models for predicting AQI are trained and tested using a variety of statistical techniques like Linear regression, Lasso regression, KNN regression, and random Forest regression This Root Mean Square Error (RMSE) found for Linear regression, Lasso regression, KNN regression, and random Forest regression are 31.01, 29,45, 37.09 and 28.3. From all four models, random forest regression was more accurate than the other three regression models in estimating PM 2.5 levels in India’s smart city.
Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-co...
Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks.
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