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DASIP 2023: Toulouse, France
- Miguel Chavarrías

, Alfonso Rodríguez
:
Design and Architecture for Signal and Image Processing - 16th International Workshop, DASIP 2023, Toulouse, France, January 16-18, 2023, Proceedings. Lecture Notes in Computer Science 13879, Springer 2023, ISBN 978-3-031-29969-8
Methods and Applications
- Ophélie Renaud, Dylan Gageot

, Karol Desnos
, Jean-François Nezan
:
SCAPE: HW-Aware Clustering of Dataflow Actors for Tunable Scheduling Complexity. 3-14 - Pedro L. Cebrián

, Alberto Martín-Pérez
, Manuel Villa
, Jaime Sancho
, Gonzalo Rosa
, Guillermo Vázquez
, Pallab Sutradhar
, Alejandro Martinez de Ternero
, Miguel Chavarrías
, Alfonso Lagares
, Eduardo Juárez
, César Sanz
:
Deep Recurrent Neural Network Performing Spectral Recurrence on Hyperspectral Images for Brain Tissue Classification. 15-27 - Guillermo Vázquez

, Manuel Villa
, Alberto Martín-Pérez
, Jaime Sancho
, Gonzalo Rosa
, Pedro L. Cebrián
, Pallab Sutradhar
, Alejandro Martinez de Ternero
, Miguel Chavarrías
, Alfonso Lagares
, Eduardo Juárez
, César Sanz
:
Brain Blood Vessel Segmentation in Hyperspectral Images Through Linear Operators. 28-39 - Tomás Malcata

, Nuno Sebastião
, Tiago Dias
, Nuno Roma
:
Neural Network Predictor for Fast Channel Change on DVB Set-Top-Boxes. 40-52
Hardware Architectures and Implementations
- Hana Krichene

, Rohit Prasad
, Ayoub Mouhagir
:
AINoC: New Interconnect for Future Deep Neural Network Accelerators. 55-69 - Mariusz Grabowski

, Tomasz Kryjak
:
Real-Time FPGA Implementation of the Semi-global Matching Stereo Vision Algorithm for a 4K/UHD Video Stream. 70-81 - Florian Meisel

, David Volz
, Christoph Spang
, Dat Tran
, Andreas Koch
:
TaPaFuzz - An FPGA-Accelerated Framework for RISC-V IoT Graybox Fuzzing. 82-94 - Daniel de Oliveira Rubiano, Guilherme Korol, Antonio Carlos Schneider Beck:

Adaptive Inference for FPGA-Based 5G Automatic Modulation Classification. 95-106 - Majdi Richa, Jean-Christophe Prévotet, Mickaël Dardaillon, Mohamad Mroué

, Abed Ellatif Samhat
:
High-Level Online Power Monitoring of FPGA IP Based on Machine Learning. 107-119

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