Virtual Network Functions allow the effective separation between hardware and network functionality, a strong paradigm shift from previously tightly integrated monolithic, vendor, and technology dependent deployments. In this virtualized paradigm, all aspects of network operations can be made to deploy on demand, dynamically scale, as well as be shared and interworked in ways that mirror behaviors of general cloud computing. To date, although seeing rising demand, distributed ledger technology remains largely incompatible in such elastic deployments, by its nature as functioning as an immutable record store. This work focuses on the structural incompatibility of current blockchain designs and proposes a novel, temporal blockchain design built atop federated byzantine agreement, which has the ability to dynamically scale and be packaged as a Virtual Network Function (VNF) for the 5G Core.
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http://dx.doi.org/10.3390/s20185281 | DOI Listing |
Micromachines (Basel)
October 2024
Third Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou 510630, China.
Accurate measurement of the vibrotactile acuities of the human torso is the key to designing effective torso-worn vibrotactile displays for healthcare applications such as navigation aids for visually impaired persons. Although efforts have been made to measure vibrotactile acuities, there remains a lack of systematic studies addressing the spatial, temporal, and intensity-related aspects of vibrotactile sensitivity on the human torso. In this work, a torso-worn vibrotactile belt consisting of two crossed coin motor arrays was designed and a psychophysical study was carried out to measure the spatial, temporal, and intensity-related vibrotactile acuities of a set of human subjects wearing the designed belt.
View Article and Find Full Text PDFSensors (Basel)
August 2024
Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China.
Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently.
View Article and Find Full Text PDFSensors (Basel)
June 2024
College of Information Science Technology, Hainan Normal University, Haikou 571158, China.
The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions.
View Article and Find Full Text PDFNeuroimage
August 2024
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China. Electronic address:
The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project.
View Article and Find Full Text PDFPeerJ Comput Sci
March 2024
Computer Science, COMSATS University Islamabad, Abbottabad, KpK, Pakistan.
Background: Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of RNN known as long short-term memory (LSTM) architecture, which updates recurrent weights to overcome the vanishing gradient problem.
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