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Publications

2020

A P2P Electricity Negotiation Agent Systems in Urban Smart Grids

Authors
de Alba, FL; Briones, AG; Chamoso, P; Pinto, T; Vale, ZA; Corchado, JM;

Publication
Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020, L'Aquila, Italy, 17-19 June 2020.

Abstract

2020

Teaching Robotics with a Simulator Environment Developed for the Autonomous Driving Competition

Authors
Fernandes, D; Pinheiro, F; Dias, A; Martins, A; Almeida, J; Silva, E;

Publication
ROBOTICS IN EDUCATION: CURRENT RESEARCH AND INNOVATIONS

Abstract
Teaching robotics based on challenge of our daily lives is always more motivating for students and teachers. Several competitions of self-driving have emerged recently, challenging students and researchers to develop solutions addressing the autonomous driving systems. The Portuguese Festival Nacional de Rob ' otica (FNR) Autonomous Driving Competition is one of those examples. Even though the competition is an exciting challenger, it requires the development of real robots, which implies several limitations that may discourage the students and compromise a fluid teaching process. The simulation can contribute to overcome this limitation and can assume an important role as a tool, providing an effortless and costless solution, allowing students and researchers to keep their focus on the main issues. This paper presents a simulation environment for FNR, providing an overall framework able to support the exploration of robotics topics like perception, navigation, data fusion and deep learning based on the autonomous driving competition.

2020

Big data analytics for future electricity grids

Authors
Kezunovic, M; Pinson, P; Obradovic, Z; Grijalva, S; Hong, T; Bessa, R;

Publication
ELECTRIC POWER SYSTEMS RESEARCH

Abstract
This paper provides a survey of big data analytics applications and associated implementation issues. The emphasis is placed on applications that are novel and have demonstrated value to the industry, as illustrated using field data and practical applications. The paper reflects on the lessons learned from initial implementations, as well as ideas that are yet to be explored. The various data science trends treated in the literature are outlined, while experiences from applying them in the electricity grid setting are emphasized to pave the way for future applications. The paper ends with opportunities and challenges, as well as implementation goals and strategies for achieving impactful outcomes.

2020

The Role of Social Networks in the Internationalisation of Startups: LinkedIn in Portuguese Context

Authors
Almeida, F; Santos, JD;

Publication
MANAGEMENT & MARKETING-CHALLENGES FOR THE KNOWLEDGE SOCIETY

Abstract
This study aims to explore the role of social networks in the internationalisation of startups. For this purpose, the social network LinkedIn is used, and two case studies of Portuguese technological startups are employed. The findings indicate that social networks can contribute to the acceleration of the internationalisation process and decrease their costs. Their relevance is greater in the initial phase of the internationalisation process. However, its relevance is limited in more advanced phases of this process. LinkedIn can be used by startups to obtain several benefits such as brand awareness, identification of new opportunities, customer feedback, among others. The results of this study are essentially useful in a practical dimension for companies that plan to start or improve their internationalisation process sustained on social networks.

2020

Decision Support System for Solar Energy Adoption

Authors
Lopes, C; Martino, D; Bandeira, N; Almeida, F;

Publication
Renewable Energy and Sustainable Development

Abstract

2020

Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning

Authors
Zamani, M; Sokolic, J; Jiang, D; Renna, F; Rodrigues, MRD; Demosthenous, A;

Publication
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS

Abstract
This paper presents an adaptable dictionary-based feature extraction approach for spike sorting offering high accuracy and low computational complexity for implantable applications. It extracts and learns identifiable features from evolving subspaces through matched unsupervised subspace filtering. To provide compatibility with the strict constraints in implantable devices such as the chip area and power budget, the dictionary contains arrays of {-1, 0 and 1} and the algorithm need only process addition and subtraction operations. Three types of such dictionary were considered. To quantify and compare the performance of the resulting three feature extractors with existing systems, a neural signal simulator based on several different libraries was developed. For noise levels sigma(N) between 0.05 and 0.3 and groups of 3 to 6 clusters, all three feature extractors provide robust high performance with average classification errors of less than 8% over five iterations, each consisting of 100 generated data segments. To our knowledge, the proposed adaptive feature extractors are the first able to classify reliably 6 clusters for implantable applications. An ASIC implementation of the best performing dictionary-based feature extractor was synthesized in a 65-nm CMOS process. It occupies an area of 0.09 mm(2) and dissipates up to about 10.48 mu W from a 1 V supply voltage, when operating with 8-bit resolution at 30 kHz operating frequency.

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