Zobrazeno 1 - 10
of 4 792
pro vyhledávání: '"Huffman Coding"'
Autor:
Yan Yu, Yanbo Chen, Haimo Zhang, Ting Lyu, Can Wang, Lindsay Wang, Yuejia Zhang, Kaigui Bian, Hong Li
Publikováno v:
IEEE Access, Vol 12, Pp 159966-159981 (2024)
In hospitals and pharmacies, the demand for efficient medication management (e.g., item storage and access) is rapidly increasing, and the scale of medication management systems is gradually expanding. Intelligent medication management systems based
Externí odkaz:
https://doaj.org/article/bc9700edad3246cd9d54b0335fb01b09
Publikováno v:
Symmetry, Vol 16, Iss 11, p 1419 (2024)
With the development of the information age, all walks of life are inseparable from the internet. Every day, huge amounts of data are transmitted and stored on the internet. Therefore, to improve transmission efficiency and reduce storage occupancy,
Externí odkaz:
https://doaj.org/article/b92eb105497742798bef47c114daab78
Autor:
Piotr Beling
Publikováno v:
SoftwareX, Vol 26, Iss , Pp 101681- (2024)
BSuccinct is a collection of software focused on compact and succinct data structures that are both space and time efficient. It is written in Rust, a programming language well suited for scientific applications due to its emphasis on reliability and
Externí odkaz:
https://doaj.org/article/059e104671dc4e21b517fb6f66cc2fdd
Publikováno v:
网络与信息安全学报, Vol 9, Pp 64-73 (2023)
The k-anonymity model is widely used as a data anonymization technique for privacy protection during the data release phase.However, with the advent of the big data era, the generation of vast amounts of data poses challenges to data storage.However,
Externí odkaz:
https://doaj.org/article/a2d30fc92a5e44deaa8995788fe57f7d
Publikováno v:
Journal of NeuroEngineering and Rehabilitation, Vol 20, Iss 1, Pp 1-17 (2023)
Abstract Background Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of mul
Externí odkaz:
https://doaj.org/article/3b3c634120624db0bcf2cd903648558c
Publikováno v:
Cybersecurity, Vol 6, Iss 1, Pp 1-11 (2023)
Abstract Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Andro
Externí odkaz:
https://doaj.org/article/eca085512873474cb197b1c0a292fa9e
Autor:
Beomseok Seo, Albert No
Publikováno v:
IEEE Access, Vol 11, Pp 140559-140568 (2023)
Transformers have excelled in natural language processing and vision domains. This leads to the intriguing proposition: can Transformers be adapted to a more generalized framework, such as understanding general finite state machines? To explore this,
Externí odkaz:
https://doaj.org/article/ace7842737334f43b8174d5323da6829
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 11, Iss 6, Pp 1902-1911 (2023)
In the compression of massive compound power quality disturbance (PQD) signals in active distribution networks, the compression ratio (CR) and reconstruction error (RE) act as a pair of contradictory indicators, and traditional compression algorithms
Externí odkaz:
https://doaj.org/article/9058b7cd1bf84f7ba5e6499660ebd818
Publikováno v:
IEEE Access, Vol 11, Pp 42751-42763 (2023)
As recent machine translation models are mostly based on the attention-based neural machine translation (NMT), many well-known models such as Transformer or bidirectional encoder representations from Transformers (BERT) have been proposed. Along with
Externí odkaz:
https://doaj.org/article/75fca3e1ee2445d68bc56067149d3b6d
Autor:
Yi-Ting Chang
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Convolutional neural networks have achieved remarkable success in computer vision research. However, to further improve their performance, network models have become increasingly complex and require more memory and computational resources. As a resul
Externí odkaz:
https://doaj.org/article/ab632507503d4c2c8d66bbb447b4f6c4