DSpace 8

DSpace is the world leading open source repository platform that enables organisations to:

  • easily ingest documents, audio, video, datasets and their corresponding Dublin Core metadata
  • open up this content to local and global audiences, thanks to the OAI-PMH interface and Google Scholar optimizations
  • issue permanent urls and trustworthy identifiers, including optional integrations with handle.net and DataCite DOI

Join an international community of leading institutions using DSpace.

The test user accounts below have their password set to the name of this software in lowercase.

  • Demo Site Administrator = dspacedemo+admin@gmail.com
  • Demo Community Administrator = dspacedemo+commadmin@gmail.com
  • Demo Collection Administrator = dspacedemo+colladmin@gmail.com
  • Demo Submitter = dspacedemo+submit@gmail.com
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Communities in DSpace

Select a community to browse its collections.

Recent Submissions

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python001
(2026-01-22)
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Test harvester
(2026-01-22)
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test
(2025-11-24) apirak punsarn
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HYBRID ARTIFICIAL INTELLIGENCE SCHEME FOR VERTICAL HANDOVER IN HETEROGENEOUS NETWORKS
(Srinakharinwirot University, 19/7/2024) Sunisa Kunarak; สุนิศา คุณารักษ์; Sunisa Kunarak; สุนิศา คุณารักษ์; sunisaku@swu.ac.th; sunisaku@swu.ac.th; Srinakharinwirot University
Current wireless communication requires high operational speed, along with consistent and robust connections. One crucial process is the handover, which involves the movement of users, necessitating the transfer of connections from one base station to another. This process must be continuous to prevent service interruptions or disruptions. This research presents a vertical handover process in heterogeneous networks using a hybrid artificial intelligence method. The proposed method employs a hybrid intelligence combining Double Q-Learning (DQL) and Long Short-Term Memory (LSTM), considering signal strength, data volume, and initial handover time as inputs for the vertical handover process. The results, compared to a hybrid intelligence using Q-Learning (QL) and Long Short-Term Memory (LSTM), indicate that the proposed method effectively reduces the rates of ping-pong effect and unnecessary handovers by an average of 13.86% and 10.68%, respectively.
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HYBRID ARTIFICIAL INTELLIGENCE SCHEME FOR VERTICAL HANDOVER IN HETEROGENEOUS NETWORKS
(Srinakharinwirot University, 19/7/2024) Sunisa Kunarak; สุนิศา คุณารักษ์; Sunisa Kunarak; สุนิศา คุณารักษ์; sunisaku@swu.ac.th; sunisaku@swu.ac.th; Srinakharinwirot University
Current wireless communication requires high operational speed, along with consistent and robust connections. One crucial process is the handover, which involves the movement of users, necessitating the transfer of connections from one base station to another. This process must be continuous to prevent service interruptions or disruptions. This research presents a vertical handover process in heterogeneous networks using a hybrid artificial intelligence method. The proposed method employs a hybrid intelligence combining Double Q-Learning (DQL) and Long Short-Term Memory (LSTM), considering signal strength, data volume, and initial handover time as inputs for the vertical handover process. The results, compared to a hybrid intelligence using Q-Learning (QL) and Long Short-Term Memory (LSTM), indicate that the proposed method effectively reduces the rates of ping-pong effect and unnecessary handovers by an average of 13.86% and 10.68%, respectively.