Senior Lecturer and Researcher
Call: +34 976 762 856
Email: rcasas@unizar.es
Address: Edificio I+D+i, Campus Río Ebro, C/ Mariano Esquillor s/n 50018 (Zaragoza, Spain)
ABOUT ME
Roberto Casas holds a Master Degree in Electrical Engineering and then a PhD in Electronic Engineering both at University of Zaragoza (Spain). He has worked as researcher and teacher in degree, master and doctorate courses at the School of Arts and Communication in Malmö University (Sweden), at the Technical University of Catalonia (Spain) and at the University of Zaragoza. Since 2011 he coordinates HOWLab (Human Openware Research Lab) at Aragon Institute of Engineering Research. His fundamental research interests are the electronics and communications behind Internet of Things and the interaction among technology and design.
He has 30 publications in indexed journals (such as IEEE Computer, IEEE Pervasive Computing, Elsevier Computer Communications, IEEE Wireless Communications and Sensors), 9 book chapters and 25 communications to peer reviewed conferences. He has participated in 20 national and three European projects; being principal investigator of 7 national and scientific coordinator of one European research project. Derived from the research done in collaboration with companies (24 private contracts), the PI is co-inventor of 10 patents, 4 of them under industrial exploitation.
PUBLICATIONS
2022
Siguín, Marta; Blanco, Teresa; Rossano, Federico; Casas, Roberto
Modular E-Collar for Animal Telemetry: An Animal-Centered Design Proposal Journal Article
In: Sensors, vol. 22, no. 1, 2022, ISSN: 1424-8220.
@article{s22010300,
title = {Modular E-Collar for Animal Telemetry: An Animal-Centered Design Proposal},
author = {Marta Siguín and Teresa Blanco and Federico Rossano and Roberto Casas},
url = {https://www.mdpi.com/1424-8220/22/1/300},
doi = {10.3390/s22010300},
issn = {1424-8220},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {1},
abstract = {Animal telemetry is a subject of great potential and scientific interest, but it shows design-dependent problems related to price, flexibility and customization, autonomy, integration of elements, and structural design. The objective of this paper is to provide solutions, from the application of design, to cover the niches that we discovered by reviewing the scientific literature and studying the market. The design process followed to achieve the objective involved a development based on methodologies and basic design approaches focused on the human experience and also that of the animal. We present a modular collar that distributes electronic components in several compartments, connected, and powered by batteries that are wirelessly recharged. Its manufacture is based on 3D printing, something that facilitates immediacy in adaptation and economic affordability. The modularity presented by the proposal allows for adapting the size of the modules to the components they house as well as selecting which specific modules are needed in a project. The homogeneous weight distribution is transferred to the comfort of the animal and allows for a better integration of the elements of the collar. This device substantially improves the current offer of telemetry devices for farming animals, thanks to an animal-centered design process.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Animal telemetry is a subject of great potential and scientific interest, but it shows design-dependent problems related to price, flexibility and customization, autonomy, integration of elements, and structural design. The objective of this paper is to provide solutions, from the application of design, to cover the niches that we discovered by reviewing the scientific literature and studying the market. The design process followed to achieve the objective involved a development based on methodologies and basic design approaches focused on the human experience and also that of the animal. We present a modular collar that distributes electronic components in several compartments, connected, and powered by batteries that are wirelessly recharged. Its manufacture is based on 3D printing, something that facilitates immediacy in adaptation and economic affordability. The modularity presented by the proposal allows for adapting the size of the modules to the components they house as well as selecting which specific modules are needed in a project. The homogeneous weight distribution is transferred to the comfort of the animal and allows for a better integration of the elements of the collar. This device substantially improves the current offer of telemetry devices for farming animals, thanks to an animal-centered design process.
Gascón, Alberto; Casas, Roberto; Buldain, David; Marco, Álvaro
Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City Journal Article
In: Sensors, vol. 22, no. 2, 2022, ISSN: 1424-8220.
@article{s22020586,
title = {Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City},
author = {Alberto Gascón and Roberto Casas and David Buldain and Álvaro Marco},
url = {https://www.mdpi.com/1424-8220/22/2/586},
doi = {10.3390/s22020586},
issn = {1424-8220},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {2},
abstract = {Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback-Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback-Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.
2021
Casas, Roberto; Hermosa, Arturo; Marco, Álvaro; Blanco, Teresa; Zarazaga-Soria, Francisco Javier
Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure Journal Article
In: Applied Sciences, vol. 11, no. 3, 2021, ISSN: 2076-3417.
@article{app11031240,
title = {Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure},
author = {Roberto Casas and Arturo Hermosa and Álvaro Marco and Teresa Blanco and Francisco Javier Zarazaga-Soria},
url = {https://www.mdpi.com/2076-3417/11/3/1240},
doi = {10.3390/app11031240},
issn = {2076-3417},
year = {2021},
date = {2021-01-01},
journal = {Applied Sciences},
volume = {11},
number = {3},
abstract = {Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest.
Calavia, Belén; Blanco, Teresa; Casas, Roberto
Fostering creativity as a problem-solving competence through design: Think-Create-Learn, a tool for teachers Journal Article
In: Thinking Skills and Creativity, vol. 39, pp. 100761, 2021, ISSN: 1871-1871.
@article{CALAVIA2021100761,
title = {Fostering creativity as a problem-solving competence through design: Think-Create-Learn, a tool for teachers},
author = {Belén Calavia and Teresa Blanco and Roberto Casas},
url = {https://www.sciencedirect.com/science/article/pii/S1871187120302352},
doi = {https://doi.org/10.1016/j.tsc.2020.100761},
issn = {1871-1871},
year = {2021},
date = {2021-01-01},
journal = {Thinking Skills and Creativity},
volume = {39},
pages = {100761},
abstract = {Although there is no doubt about the relevance of creativity within education, theory has not been always translated into the practical level, for many reasons. In this paper we analyse the state of art, studying the methods through which creativity is understood and applied by teachers, and identifying problems and opportunities. Accordingly, we conducted a literature review to identify what should be considered to foster creativity in classrooms; from this review, we define fifteen key indicators of creativity in education: incorporation, practicality, novel, atmosphere, stimulation, analysis, cooperation, intrinsic motivation, participation, flexibility, uncertainty, time, divergence, self-evaluation, and redefinition. Based on these indicators, we provide a methodological proposal and a set of practical resources to help the teacher to encourage creativity in any classroom. ‘Think-Create-Learn’ relies on open, accessible, and intuitive design-based tools, facing challenges through a creative, problem-solving approach; connecting the contents with the student’s interests and reality; and generating new competency learning possibilities. The assessment of the methodology, with teachers and students, demonstrates its positive integration into the lines of current teaching curriculums, its validity to support mentioned factors, and its ability to aid teachers to produce more creative people. In short, this paper evidences how design discipline and the methodology proposed could have a relevant role in the creativity development inside educational centres.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Although there is no doubt about the relevance of creativity within education, theory has not been always translated into the practical level, for many reasons. In this paper we analyse the state of art, studying the methods through which creativity is understood and applied by teachers, and identifying problems and opportunities. Accordingly, we conducted a literature review to identify what should be considered to foster creativity in classrooms; from this review, we define fifteen key indicators of creativity in education: incorporation, practicality, novel, atmosphere, stimulation, analysis, cooperation, intrinsic motivation, participation, flexibility, uncertainty, time, divergence, self-evaluation, and redefinition. Based on these indicators, we provide a methodological proposal and a set of practical resources to help the teacher to encourage creativity in any classroom. ‘Think-Create-Learn’ relies on open, accessible, and intuitive design-based tools, facing challenges through a creative, problem-solving approach; connecting the contents with the student’s interests and reality; and generating new competency learning possibilities. The assessment of the methodology, with teachers and students, demonstrates its positive integration into the lines of current teaching curriculums, its validity to support mentioned factors, and its ability to aid teachers to produce more creative people. In short, this paper evidences how design discipline and the methodology proposed could have a relevant role in the creativity development inside educational centres.
2020
Vizárraga, Jorge; Casas, Roberto; Marco, Álvaro; Buldain, David J
Dimensionality Reduction for Smart IoT Sensors Journal Article
In: Electronics, vol. 9, no. 12, 2020, ISSN: 2079-9292.
@article{electronics9122035,
title = {Dimensionality Reduction for Smart IoT Sensors},
author = {Jorge Vizárraga and Roberto Casas and Álvaro Marco and David J Buldain},
url = {https://www.mdpi.com/2079-9292/9/12/2035},
doi = {10.3390/electronics9122035},
issn = {2079-9292},
year = {2020},
date = {2020-01-01},
journal = {Electronics},
volume = {9},
number = {12},
abstract = {Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation.