José Angel Miranda Calero

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Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such app ...

Coarse-Grain Reconfigurable Arrays (CGRAs) represent emerging low-power architectures designed to accelerate Compute-Intensive Loops (CILs). The effectiveness of CGRAs in providing acceleration relies on the quality of mapping: how efficiently the CIL is c ...

This paper addresses the optimization of embedded platforms to meet the computing and real-time requirements of cyber-physical systems and IoT applications, including embedded intelligence. In this context, schedulers are vital in enhancing processor utili ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there ...

E-textiles are rapidly emerging as an important area of electronic circuit applications. It also facilitates many socially important applications such as personalized health, elderly care, and smart agriculture. However, the environmental impact and sustai ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

WEMAC is a unique open multi-modal dataset that comprises physiological, speech, and self-reported emotional data records of 100 women, targeting Gender-based Violence detection. Emotions were elicited through visualizing a validated video set using an imm ...

In this work, we propose an open-hardware low-power coarse-grained reconfigurable array connected to a lightweight microcontroller and enclosed in an application mapping framework. The latter provides complete support to configure kernels in the reconfigur ...

In this work, we present eXtendible Heterogeneous Energy-Efficient Platform (X-HEEP), a configurable and extendible single-core RISC-V-based ultra-low-power microcontroller. X-HEEP can be used standalone as a low-cost microcontroller, or it can be integrat ...

Most of today's wearable technology provides seamless cardiac activity monitoring. Specifically, the vast majority employ Photoplethysmography (PPG) sensors to acquire blood volume pulse information, which is further analysed to extract useful and physiolo ...

José Angel Miranda Calero

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Solving the Cold-Start Problem for the Edge: Clustering and Adaptive Deep Learning for Emotion Detection

SAT-based Exact Modulo Scheduling Mapping for Resource-Constrained CGRAs

Dynamic Scheduling for Event-Driven Embedded Industrial Applications

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Personalized Clustering for Emotion Recognition Improvement

Multi-Partner Project: Sustainable Textile Electronics (STELEC)

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

WEMAC: Women and Emotion Multi-modal Affective Computing dataset

An Open-Hardware Coarse-Grained Reconfigurable Array for Edge Computing

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller

Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge

DeepBindi: An End-to-End Fear Detection System Optimized for Extreme-Edge Deployment