American Psychological Association 6th edition

Sarangi, S., & Baas, B. (2023). Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs. Integration: The VLSI Journal, 88, 156-165. https://doi.org/10.1016/j.vlsi.2022.09.015

ISO-690 (author-date, English)

SARANGI, Satyabrata und BAAS, Bevan, 2023. Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs. Integration: The VLSI Journal. 1 Januar 2023. Vol. 88, , p. 156-165. DOI 10.1016/j.vlsi.2022.09.015.

Modern Language Association 9th edition

Sarangi, S., und B. Baas. „Energy-Efficient Canonical Huffman Decoders on Many-Core Processor Arrays and FPGAs.“. Integration: The VLSI Journal, Bd. 88, Januar 2023, S. 156-65, https://doi.org/10.1016/j.vlsi.2022.09.015.

Mohr Siebeck - Recht (Deutsch - Österreich)

Sarangi, Satyabrata/Baas, Bevan: Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs., Integration: The VLSI Journal 2023, 156-165.

Emerald - Harvard

Sarangi, S. und Baas, B. (2023), „Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs.“, Integration: The VLSI Journal, Vol. 88, S. 156-165.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.