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Digital Library

Recent Submissions

Item

Arti¯cial Intelligence Implementation in Library Information Systems: Current Trends and Future Studies (S3)

(2025-01-04) Albertoes Pramoekti Narendra Christine Dewi Lanyta Setyani Gunawan and Ambrosius Sindu Ardi

Item

Artificial Intelligence Implementation in Library Information Systems: Current Trends and Future Studies

(2025-01-04) Albertoes Pramoekti, Narendra Christine Dewi, Lanyta Setyani Gunawan and Ambrosius Sindu Ardi

The field of Library Information Systems (LIS) has transformed with the rise of Artificial Intelligence (AI), enhancing library operations, services, and user experiences. AI improves user satisfaction by streamlining access to resources and providing librarians with tools for collection management, information retrieval, and data-driven decision-making. It enables analysis of user behavior and trends, optimizing resource allocation and collection development. By leveraging AI to assess circulation data and usage patterns, librarians can better anticipate user needs and future trends. This study examines recent advancements in AI integration within LIS over the past five years, focusing on the methodologies, tools, and algorithms used. Through a systematic literature review, the study identifies key trends, challenges, and examples of AI applications in LIS, providing insights and recommendations for future research and implementation.

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An Energy Efficiency Perspective on Training for Fading Channels

(University of Nebraska-Lincoln, Lincoln, NE 68588, 2007) Mustafa Cenk Gursoy

In this paper, the bit energy requirements of training-based transmission over block Rayleigh fading channels are studied. Pilot signals are employed to obtain the minimum mean-square-error (MMSE) estimate of the channel fading coefficients. Energy efficiency is analyzed in the worst case scenario where the channel estimate is assumed to be perfect and the error in the estimate is considered as another source of additive Gaussian noise. It is shown that bit energy requirement grows without bound as the SNR goes to zero, and the minimum bit energy is achieved at a nonzero SNR value below which one should not operate. The effect of the block length on both the minimum bit energy and the SNR value at which the minimum is achieved is investigated. Flash training schemes are analyzed and shown to improve the energy efficiency in the low-SNR regime. Energy efficiency analysis is also carried out when peak power constraints are imposed on pilot signals.