Novel Network Paradigms: Microfluidic and M2M Communications

The present thesis focuses on two appealing paradigms that are expected to characterize the next generation of communication systems: microfluidic networking and Machine to Machine (M2M) Communications. Concerning the former topic, we show how it is possible to introduce switching and routing mechanism in microfluidic systems. We define some simple mathematical models that capture the macroscopic behavior of droplets in microfluidic networks. Then, we use them to implement a simulator that is able to reproduce the motion and predict the path of droplets in a generic microfluidic system. We validate the simulator and apply it to design a network with bus topology. Finally, we prove the feasibility of attaining molecular communication in this domain by describing a simple protocol that exploits droplets length/interdistance modulation to send information. The research activity on M2M, instead, is aimed at the investigation of two critical issues that are expected to affect Machine-Type Communication (MTC), i.e. energy efficiency and massive access. Regarding energy efficiency, we address the problem of delivering a fixed data payload over a Rayleigh fading wireless channel with the purpose of minimizing the average total energy cost, given by the sum of the transmit energy and an overhead circuit energy, to complete it. This scenario is well suited for uplink cellular MTC in future 5G Internet of Things (IoT) use cases, where the focus is more on device energy efficiency than on throughput. We describe the optimal transmission policies to be used under various coordinated access scenarios with different levels of channel state information and transmitter/receiver capabilities, and show the corresponding theoretical bounds. In the last part of the work, we study the asymptotic performance of uncoordinated access schemes with Multi Packet Reception (MPR) and Successive Interference Cancellation (SIC) techniques for contention resolution at the receiver. The corresponding results in terms of throughput in a massive access M2M scenario are finally evaluated and discussed

Introducing purely hydrodynamic networking mechanisms in microfluidic systems

Microfluidic is a multidisciplinary field with prac- tical applications to the design of systems, called Lab-on-a- Chip (LoC), where tiny volumes of fluids are circulated through channels with millimeter size and driven into structures where precise chemical/physical processes take place. One subcategory of microfluidic is droplet-based microfluidic, which disperse discrete volumes of fluids into a continuous stream of another immiscible fluid, which act as droplet carrier. Droplets can then be moved, merged, split, or processed in many other ways by suitably managing the hydrodynamic parameters of the LoC. A very interesting research challenge consists in developing basic microfluidic structures able to interconnect specialized LoCs by means of a flexible and modular microfluidic network. The aim of this paper is to exploit the properties of droplet- based microfluidics to realize purely hydrodynamic microfluidic elements that provide basic networking functionalities, such as addressing and switching. We define some simple mathematical models that capture the macroscopic behavior of droplets in microfluidic networks and use such models to design and analyze a simple microfluidic network system with bus topology

Estimating an individual's oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study

Measurement of oxygen uptake during exercise ([Formula: see text]) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling [Formula: see text] from easy-to-obtain inputs, such as heart rate, mechanical power output, cadence and respiratory frequency. To this end, a recurrent neural network was trained from laboratory cycling data to predict [Formula: see text] values. Data were collected on 7 amateur cyclists during a graded exercise test, two arbitrary protocols (Prot-1 and -2) and an "all-out" Wingate test. In Trial-1, a neural network was trained with data from a graded exercise test, Prot-1 and Wingate, before being tested against Prot-2. In Trial-2, a neural network was trained using data from the graded exercise test, Prot-1 and 2, before being tested against the Wingate test. Two analytical models (Models 1 and 2) were used to compare the predictive performance of the neural network. Predictive performance of the neural network was high during both Trial-1 (MAE = 229(35) mlO2min-1, r = 0.94) and Trial-2 (MAE = 304(150) mlO2min-1, r = 0.89). As expected, the predictive ability of Models 1 and 2 deteriorated from Trial-1 to Trial-2. Results suggest that recurrent neural networks have the potential to predict the individual [Formula: see text] response from easy-to-obtain inputs across a wide range of cycling intensities

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

SerpinA1 levels in amyotrophic lateral sclerosis patients: An exploratory study

Background: SerpinA1, a serine protease inhibitor, is involved in the modulation of microglial-mediated inflammation in neurodegenerative diseases. We explored SerpinA1 levels in cerebrospinal fluid (CSF) and serum of amyotrophic lateral sclerosis (ALS) patients to understand its potential role in the pathogenesis of the disease. Methods: SerpinA1, neurofilament light (NfL) and heavy (NfH) chain, and chitinase-3-like protein-1 (CHI3L1) were determined in CSF and serum of ALS patients (n = 110) and healthy controls (n = 10) (automated next-generation ELISA), and correlated with clinical parameters, after identifying three classes of progressors (fast, intermediate, slow). Biomarker levels were analyzed for diagnostic power and association with progression and survival. Results: SerpinA1serum was significantly decreased in ALS (median: 1032 μg/mL) compared with controls (1343 μg/mL) (p = 0.02). SerpinA1CSF was elevated only in fast progressors (8.6 μg/mL) compared with slow (4.43 μg/mL, p = 0.01) and intermediate (4.42 μg/mL, p = 0.03) progressors. Moreover, SerpinA1CSF correlated with neurofilament and CHI3L1 levels in CSF. Contrarily to SerpinA1CSF , neurofilament and CHI3L1 concentrations in CSF correlated with measures of disease progression in ALS, while SerpinA1serum mildly related with time to generalization (rho = 0.20, p = 0.04). In multivariate analysis, the ratio between serum and CSF SerpinA1 (SerpinA1 ratio) and NfHCSF were independently associated with survival. Conclusions: Higher SerpinA1CSF levels are found in fast progressors, suggesting SerpinA1 is a component of the neuroinflammatory mechanisms acting upon fast-progressing forms of ALS. Both neurofilaments or CHI3L1CSF levels outperformed SerpinA1 at predicting disease progression rate in our cohort, and so the prognostic value of SerpinA1 alone as a measure remains inconclusive

Microfluidic networking: modelling and analysis

Droplets microfluidics refers to manipulation and control of little amounts of fluids flowing into channels of micro-scale size. Our aim, pursued in the present thesis, is to design a network formed by such microchannels and define a model to properly route the droplets inside them. However, this kind of study relies on the preliminary deep knowledge of microfluidic flow dynamics and typical propagation characteristics. Accordingly, we begin our dissertation by introducing the physical laws that govern microfluidics. Then, we discuss the current understandings about droplets formation, transport and their behavior in bifurcating channels, corroborating all with simulative results. Furthermore, we show how such concepts can be integrated in our networking solution and, lastly, we implement a microfluidic network with bus topology and illustrate its performanc

Novel Network Paradigms: Microfluidic and M2M Communications

The present thesis focuses on two appealing paradigms that are expected to characterize the next generation of communication systems: microfluidic networking and Machine to Machine (M2M) Communications. Concerning the former topic, we show how it is possible to introduce switching and routing mechanism in microfluidic systems. We define some simple mathematical models that capture the macroscopic behavior of droplets in microfluidic networks. Then, we use them to implement a simulator that is able to reproduce the motion and predict the path of droplets in a generic microfluidic system. We validate the simulator and apply it to design a network with bus topology. Finally, we prove the feasibility of attaining molecular communication in this domain by describing a simple protocol that exploits droplets length/interdistance modulation to send information. The research activity on M2M, instead, is aimed at the investigation of two critical issues that are expected to affect Machine-Type Communication (MTC), i.e. energy efficiency and massive access. Regarding energy efficiency, we address the problem of delivering a fixed data payload over a Rayleigh fading wireless channel with the purpose of minimizing the average total energy cost, given by the sum of the transmit energy and an overhead circuit energy, to complete it. This scenario is well suited for uplink cellular MTC in future 5G Internet of Things (IoT) use cases, where the focus is more on device energy efficiency than on throughput. We describe the optimal transmission policies to be used under various coordinated access scenarios with different levels of channel state information and transmitter/receiver capabilities, and show the corresponding theoretical bounds. In the last part of the work, we study the asymptotic performance of uncoordinated access schemes with Multi Packet Reception (MPR) and Successive Interference Cancellation (SIC) techniques for contention resolution at the receiver. The corresponding results in terms of throughput in a massive access M2M scenario are finally evaluated and discussed.La presente tesi si focalizza sullo studio di due importanti paradigmi che si prevede possano caratterizzare i sistemi di comunicazione di prossima generazione: le reti microfluidiche e le comunicazioni Machine to Machine (M2M). Riguardo alle reti microfluidiche, in questo lavoro illustriamo come sia possibile introdurre elementi di switch e meccanismi di routing all’interno di sistemi microfluidici. Definiamo poi alcuni semplici modelli matematici che descrivono il comportamento macroscopico di gocce all’interno di tali reti. Questi ultimi sono quindi sfruttati per implementare un simulatore che è capace di riprodurre il movimento e predire il percorso delle gocce in un generico sistema microfluidico. Dopo averlo validato sperimentalmente, il simulatore è impiegato per progettare una rete microfluidica con topologia a bus. Infine, viene dimostrato come sia possibile realizzare comunicazioni molecolari in questo ambito tramite la formalizzazione e la descrizione di un protocollo che sfrutta la modulazione della lunghezza/interdistanza delle gocce per trasferire informazione. L’attività di ricerca in merito alle comunicazioni M2M, invece, è finalizzata allo studio di due importanti criticità insite nelle Machine-Type Communications (MTCs), ovvero l’efficienza energetica e l’accesso simultaneo di massa (massive access). Per quanto concerne l’efficienza energetica, viene affrontato il problema di trasmettere un payload di una certa lunghezza fissata attraverso un canale wireless affetto da Rayleigh fading con lo scopo di minimizzare il costo totale medio dell’utente finale, dato dalla somma dell’energia di trasmissione e di quella di circuito, per completare l’operazione. Tale scenario ben si applica al contesto di trasmissioni cellulari per applicazioni di tipo IoT nelle future reti 5G, dove l’attenzione è rivolta maggiormente all’efficienza energetica dei dispositivi rispetto al throughput, in quanto le UE hanno tipicamente capacità computazionali ed energetiche esigue e si limitano ad inviare sporadicamente pacchetti molto brevi. Vengono quindi descritte le strategie ottime di trasmissione da adottare in un contesto di accesso coordinato a seconda del livello di dettaglio sulle informazioni di canale e delle potenzialità di trasmettitore/ricevitore, illustrando i corrispondenti limiti teorici. Nell’ultima parte del lavoro vengono studiate le prestazioni asintotiche di schemi di accesso non coordinati quando si utilizzano tecniche di Multi Packet Reception (MPR) e Successive Interference Cancellation (SIC) per la risoluzione delle collisioni al ricevitore. I risultati corrispondenti, in termini di throughput, per uno scenario M2M con massive access sono infine ricavati e discussi