Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications

Effective memory management for mobile environments

Smartphones, tablets, and other mobile devices exhibit vastly different constraints compared to regular or classic computing environments like desktops, laptops, or servers. Mobile devices run dozens of so-called “apps” hosted by independent virtual machines (VM). All these VMs run concurrently and each VM deploys purely local heuristics to organize resources like memory, performance, and power. Such a design causes conflicts across all layers of the software stack, calling for the evaluation of VMs and the optimization techniques specific for mobile frameworks. In this dissertation, we study the design of managed runtime systems for mobile platforms. More specifically, we deepen the understanding of interactions between garbage collection (GC) and system layers. We develop tools to monitor the memory behavior of Android-based apps and to characterize GC performance, leading to the development of new techniques for memory management that address energy constraints, time performance, and responsiveness. We implement a GC-aware frequency scaling governor for Android devices. We also explore the tradeoffs of power and performance in vivo for a range of realistic GC variants, with established benchmarks and real applications running on Android virtual machines. We control for variation due to dynamic voltage and frequency scaling (DVFS), Just-in-time (JIT) compilation, and across established dimensions of heap memory size and concurrency. Finally, we provision GC as a global service that collects statistics from all running VMs and then makes an informed decision that optimizes across all them (and not just locally), and across all layers of the stack. Our evaluation illustrates the power of such a central coordination service and garbage collection mechanism in improving memory utilization, throughput, and adaptability to user activities. In fact, our techniques aim at a sweet spot, where total on-chip energy is reduced (20–30%) with minimal impact on throughput and responsiveness (5–10%). The simplicity and efficacy of our approach reaches well beyond the usual optimization techniques

Dynamically reconfigurable architecture for embedded computer vision systems

The objective of this research work is to design, develop and implement a new architecture which integrates on the same chip all the processing levels of a complete Computer Vision system, so that the execution is efficient without compromising the power consumption while keeping a reduced cost. For this purpose, an analysis and classification of different mathematical operations and algorithms commonly used in Computer Vision are carried out, as well as a in-depth review of the image processing capabilities of current-generation hardware devices. This permits to determine the requirements and the key aspects for an efficient architecture. A representative set of algorithms is employed as benchmark to evaluate the proposed architecture, which is implemented on an FPGA-based system-on-chip. Finally, the prototype is compared to other related approaches in order to determine its advantages and weaknesses

Self-Test Mechanisms for Automotive Multi-Processor System-on-Chips

L'abstract è presente nell'allegato / the abstract is in the attachmen

Future Trends in Advanced Materials and Processes

The Special Issue “Future Trends in Advanced Materials and Processes” contains original high-quality research papers and comprehensive reviews addressing the relevant state-of-the-art topics in the area of materials focusing on relevant or innovative applications such as radiological hazard evaluations of non-metallic materials, composite materials' characterization, geopolymers, metallic biomaterials, etc

Magnesium phosphate cements formulated with low grade magnesium oxide incorporating phase change materials for thermal energy storage

Magnesium Phosphate Cement (MPC) has become an essential reference for investigators seeking alternatives to the use of Ordinary Portland Cement (OPC) in building sector because of its high environmental impact. The research group developed a MPC formulated with low-grade MgO (LG-MgO) by-product, which could be considered as a sustainable MPC (sust-MPC). This research focuses on the incorporation of different percentages of Microencapsulated Phase Change Materials (MPCM) into sust-MPC, due to their ability to reduce energy consumption of heating, ventilating, and air conditioning (HVAC) systems. The study consists of an exhaustive characterization of thermal sustainable MPC (TS-MPC) dosages which incorporate air-entraining additive (AEA) and MPCM to improve their thermal behaviour. Thus, TS-MPC would reduce the use of HVAC systems contributing to the decrease of CO2 emissions and increasing energy efficiency in buildings. Moreover, properties such as bulk density, porosity, thermal conductivity, modulus of elasticity, compressive strength, and flexural strength are analysed to evaluate the potential use of these cements as a part of a passive conditioning system. Results show the proper behaviour of these cements to reduce thermal oscillation in buildings. Experimental results demonstrated the relation between the amount of the MPCM and the AEA percentage as well as the thermal and mechanical properties of the TS-MPC due to their contribution to increase the porosity. Furthermore, it should be noted the increase of porosity and the reduction of thermal conductivity of the optimal formulation, which are 60% higher and 50% lower than the sust-MPC obtained without MPCM and additive, respectively