Feasibility study and porting of the damped least square algorithm on FPGA

Modern embedded computing platforms used within Cyber-Physical Systems (CPS) are nowadays leveraging more and more often on heterogeneous computing substrates, such as newest Field Programmable Gate Array (FPGA) devices. Compared to general purpose platforms, which have a fixed datapath, FPGAs provide designers the possibility of customizing part of the computing infrastructure, to better shape the execution on the application needs/features, and offer high efficiency in terms of timing and power performance, while naturally featuring parallelism. In the context of FPGA-based CPSs, this article has a two fold mission. On the one hand, it presents an analysis of the Damped Least Square (DLS) algorithm for a perspective hardware implementation. On the other hand, it describes the implementation of a robotic arm controller based on the DLS to numerically solve Inverse Kinematics problems over a heterogeneous FPGA. Assessments involve a Trossen Robotics WidowX robotic arm controlled by a Digilent ZedBoard provided with a Xilinx Zynq FPGA that computes the Inverse Kinematic

Reconfigurable Adaptive Multiple Transform Hardware Solutions for Versatile Video Coding

Computer aided design is nowadays a must to quickly provide optimized circuits, to cope with stringent time to market constraints, and to be able to guarantee colliding constrained requirements. Design automation is exploited, whenever possible, to speed up the design process and relieve the developers from error prone customization, optimization and tuning phases. In this work we study the possibility of adopting automated algorithms for the optimization of reconfigurable multiple constant multiplication circuits. In particular, an exploration of novel reconfigurable Adaptive Multiple Transform circuital solutions adoptable in video coding applications has been conducted. These solutions have also been compared with the unique similar work at the state of the art, revealing to be beneficial under certain constraints. Moreover, the proposed approach has been generalized with some guidelines helpful to designers facing similar problems

Selection of features based on electric power quantities for non-intrusive load monitoring

Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads

Stem/progenitor cells in fetuses and newborns: overview of immunohistochemical markers

Microanatomy of the vast majority of human organs at birth is characterized by marked differences as compared to adult organs, regarding their architecture and the cell types detectable at histology. In preterm neonates, these differences are even more evident, due to the lower level of organ maturation and to ongoing cell differentiation. One of the most remarkable finding in preterm tissues is the presence of huge amounts of stem/progenitor cells in multiple organs, including kidney, brain, heart, adrenals, and lungs. In other organs, such as liver, the completely different burden of cell types in preterm infants is mainly related to the different function of the liver during gestation, mainly focused on hematopoiesis, a function that is taken by bone marrow after birth. Our preliminary studies showed that the antigens expressed by stem/progenitors differ significantly from one organ to the next. Moreover, within each developing human tissue, reactivity for different stem cell markers also changes during gestation, according with the multiple differentiation steps encountered by each progenitor during development. A better knowledge of stem/progenitor cells of preterms will allow neonatologists to boost preterm organ maturation, favoring the differentiation of the multiple cells types that characterize each organ in at term neonates

Oxidation of allylic and benzylic alcohols to aldehydes and carboxylic acids

An oxidation of allylic and benzylic alcohols to the corresponding carboxylic acids is effected by merging a Cu-catalyzed oxidation using O2 as a terminal oxidant with a subsequent chlorite oxidation (Lindgren oxidation). The protocol was optimized to obtain pure products without chromatography or crystallization. Interception at the aldehyde stage allowed for Z/E-isomerization, thus rendering the oxidation stereoconvergent with respect to the configuration of the starting material

Effects of Uncertainty of Outlet Boundary Conditions in a Patient-Specific Case of Aortic Coarctation

Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions