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About

About

João M. P. Cardoso received his PhD degree in Electrical and Computer Engineering from the IST/UTL (Technical University of Lisbon), Lisbon, Portugal in 2001. He is currently Full Professor at the Department of Informatics Eng., Faculty of Eng. of the University of Porto, Porto, Portugal, and a research member of INESC TEC. Before, he was with the IST/UTL (2006-2008), a senior researcher at INESC-ID (2001-2009), and with the University of Algarve (1993-2006). In 2001/2002, he worked for PACT XPP Technologies, Inc., Munich, Germany. He has been involved in the organization and served as a Program Committee member for many international conferences. For example, he was general Co-Chair of IEEE/IFIP EUC’2015 and IEEE CSE’2015, General Chair of FPL’2013, General Co-Chair of ARC’2014 and ARC’2006, Program Co-Chair of ARCS’2016, DASIP’2014, and RAW’2010. He has (co-)authored over 150 scientific publications on subjects related to compilers, embedded systems, and reconfigurable computing. He has coordinated a number of research projects. He is a senior member of IEEE, a member of IEEE Computer Society, and a senior member of ACM.  His research interests include compilation techniques, domain-specific languages, reconfigurable computing, application-specific architectures, and high-performance computing with a particular emphasis in embedded computing.

Interest
Topics
Details

Details

  • Name

    João Paiva Cardoso
  • Role

    Senior Researcher
  • Since

    01st July 2011
002
Publications

2023

Electrical sensing of the plant Mimosa pudica under environmental temperatures

Authors
Lobo, MA; Cardoso, JMP; Rocha, PRF;

Publication
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Abstract
Plants gather and process information about their surroundings to make decisions that prioritize their well-being while considering the environment. These decisions are conveyed through electrical signals within and between cells, mainly in the form of action and variation potentials, in response to stimuli, including mechanical vibrations, changes in temperature, light intensity, and humidity. Although the ability of some plants, such as the Mimosa pudica, to react to sudden environmental stimuli (e.g., touch) is well known, their long-term electrical response under slow environmental changes remains not fully understood. Here, a multi-source monitoring system has been developed to collect and store electrical signals from the plant Mimosa pudica, and surrounding environmental temperature and humidity, over a period of approximately 5 days. A realtime dashboard shows the environmental temperature and variation potential (VP) from Mimosa pudica. The VP mimics the environmental temperature changes, with an associated delay. Our long-term physiological observations suggest that environmental temperature sensing in the plant Mimosa pudica can be monitored and is likely driven by bioelectricity.

2023

A Study on Hyperparameters Configurations for an Efficient Human Activity Recognition System

Authors
Ferreira, PJS; Moreira, JM; Cardoso, JMP;

Publication
CoRR

Abstract

2023

A Study on Hyperparameters Configurations for an Efficient Human Activity Recognition System

Authors
Ferreira, PJS; Mendes-Moreira, J; Cardoso, JMP;

Publication
PROCEEDINGS OF THE 8TH INTERNATIONAL WORKSHOP ON SENSOR-BASED ACTIVITY RECOGNITION AND ARTIFICIAL INTELLIGENCE, IWOAR 2023

Abstract
Human Activity Recognition (HAR) has been a popular research field due to the widespread of devices with sensors and computational power (e.g., smartphones and smartwatches). Applications for HAR systems have been extensively researched in recent literature, mainly due to the benefits of improving quality of life in areas like health and fitness monitoring. However, since persons have different motion patterns when performing physical activities, a HAR system would need to adapt to the characteristics of the user in order to maintain or improve accuracy. Mobile devices, such as smartphones, used to implement HAR systems, have limited resources (e.g., battery life). They also have difficulty adapting to the device's constraints to work efficiently for long periods. In this work, we present a kNN-based HAR system and an extensive study of the influence of hyperparameters (window size, overlap, distance function, and the value of k) and parameters (sampling frequency) on the system accuracy, energy consumption, and response time. We also study how hyperparameter configurations affect the model's performance for the users and the activities. Experimental results show that adapting the hyperparameters makes it possible to adjust the system's behavior to the user, the device, and the target service. These results motivate the development of a HAR system capable of automatically adapting the hyperparameters for the user, the device, and the service.

2023

A DSL-based runtime adaptivity framework for Java

Authors
Carvalho, T; Bispo, J; Pinto, P; Cardoso, JMP;

Publication
SOFTWAREX

Abstract
This article presents Kadabra, a Java source-to-source compiler that allows users to make code queries, code analysis and code transformations, all user-programmable using the domain-specific language LARA. We show how Kadabra can be used as the basis for developing a runtime autotuning and adaptivity framework, able to adapt existing source Java code in order to take advantage of runtime autotuning. Specifically, this article presents the framework, consisting of Kadabra and an API for runtime adaptivity. We show the use of the framework to extend Java applications with autotuning and runtime adaptivity mechanisms to target performance improvement and/or energy saving goals.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

2023

Preface ASAP 2023

Authors
Cardoso, JMP; Jimborean, A; Mentens, N; Coutinho, JGF;

Publication
34th IEEE International Conference on Application-specific Systems, Architectures and Processors, ASAP 2023, Porto, Portugal, July 19-21, 2023

Abstract
[No abstract available]

Supervised
thesis

2022

Automatic Selection of Software Code Regions for Migrating to GPUs

Author
Fábio Daniel Reis Gaspar

Institution
UP-FEUP

2022

Runtime-aware Compiler Optimizations for High-Performance Embedded Computing

Author
Pedro Miguel dos Santos Pinto

Institution
UP-FEUP

2022

Strategies for Compiler Phase Ordering Targeting CPUs

Author
João Miguel Araújo Monteiro da Rocha

Institution
UP-FEUP

2022

Programming FPGAs Using Task-Graphs and C code

Author
Luís Miguel Jardim Noites

Institution
UP-FEUP

2022

Energy-Computing Efficient Classification Techniques for Mobile-Based HAR Systems

Author
Paulo Jorge Silva Ferreira

Institution
UP-FEUP