David Atienza Alonso

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Current Systems-On-Chip (SoC) execute applications that demand extensive parallel processing; thus, the amount of processors, memories and application-specific signal pro- cessing cores is rapidly increasing. In these new Multi- Processor SoCs, (MPSoCs) on ...

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric tele-cardiology systems. A WBSNenabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly re ...

In spite of the progress in management of Atrial Fibrillation (AF), this arrhythmia is one of the major causes of stroke and heart failure. The progression of this pathology from a silent paroxysmal form (PAF) into a sustained AF can be prevented by predic ...

As the demand for high channel counts and high-resolution recordings of neural activity continues to grow, the increased power and data rate generated impose hard constraints on the telemetry capabilities of wireless implantable neural interfaces. To addre ...

Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and ...

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 ...

Risk management (RM) is a key component of the development of modern medical devices (MD) to achieve acceptable functional safety and pass the regulatory process. The emerging availability of various techniques, languages, and tools that use model-based sy ...

Personal health monitoring systems can offer a cost-effective solution for human healthcare. To extend the lifetime of health monitoring systems, we propose a near-threshold ultra-low- power multi-core architecture featuring low-power cores, yet capable of ...

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

State-of-the-art wearable devices such as embedded biomedical monitoring systems apply voltage scaling to lower as much as possible their energy consumption and achieve longer battery lifetimes. While embedded memories often rely on Error Correction Codes ...

Numerous Directed-Acyclic Graph (DAG) schedulers have been developed to improve the energy efficiency of various multi-core systems. However, the DAG monitoring modules proposed by these schedulers make a priori assumptions about the workload and relations ...

Wireless sensor nodes (WSNs) have recently evolved to include a fair amount of computational power, so that advanced signal processing algorithms can now be embedded even in this extremely low-power platforms. An increasingly successful field of applicatio ...

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase rema ...

Modern multimedia application exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the dynamic data st ...

The determination of the optical flow is a central problem in image processing, as it allows to describe how an image changes over time by means of a numerical vector field. The estimation of the optical flow is however a very complex problem, which has be ...

Simulators are still the primary tools for development and performance evaluation of applications running on massively parallel architectures. However, current virtual platforms are not able to tackle the complexity issues introduced by 1000-core future sc ...

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon th ...

Next-generation High-Performance Computing (HPC) systems need to provide outstanding performance with unprecedented energy efficiency while maintaining servers at safe thermal conditions. Air cooling presents important limitations when employed in HPC infr ...

Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. It requires new solutions that allow continuous unobstructed monitoring and reliable detectio ...

David Atienza Alonso

Chattez avec Graph Search

NoC emulation: a tool and design flow for MPSoC

Real-Time Compressed Sensing-Based Electrocardiogram Compression on Energy Constrained Wireless Body Sensors

A Patient-Specific Methodology for Prediction of Paroxysmal Atrial Fibrillation Onset

A Reconfigurable High Dynamic Range Delta-Sigma Front End with Event-Based Decimation for Bandwidth-EfficientImplantable Neural Interfaces

Containergy-A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads

Multi-Partner Project: Sustainable Textile Electronics (STELEC)

Model-Based ISO 14971 Risk Management of EEG-Based Medical Devices

Multi-Core Architecture Design for Ultra-Low-Power Wearable Health Monitoring Systems

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Energy vs. Reliability Trade-offs Exploration in Biomedical Ultra-Low Power Devices

A Unified Online Directed Acyclic Graph Flow Manager for Multicore Schedulers

Embedded Real-Time ECG Delineation Methods: a Comparative Evaluation

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Optimization Methodology of Dynamic Data Structures Based on Genetic Algorithms for Multimedia Embedded Systems

A High–Performance Parallel Implementation of the Chambolle Algorithm

Scalable instruction set simulator for thousand-core architectures running on GPGPUs

Interpreting deep learning models for epileptic seizure detection on EEG signals

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors

Design of a Two-Phase Gravity-Driven Micro-Scale Thermosyphon Cooling System for High-Performance Computing Data Centers

Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection