Architectural Improvements Towards an Efficient 16-18 Bit 100-200 MSPS ADC

As Data conversion systems continue to improve in speed and resolution, increasing demands are placed on the performance of high-speed Analog to Digital Conversion systems. This work makes a survey about all these and proposes a suitable architecture in order to achieve the desired specifications of 100-200MS/s with 16-18 bit of resolution. The main architecture is based on paralleled structures in order to achieve high sampling rate and at the same time high resolution. In order to solve problems related to Time-interleaved architectures, an advanced randomization method was introduced. It combines randomization and spectral shaping of mismatches. With a simple low-pass filter the method can, compared to conventional randomization algorithms, improve the SFDR as well as the SINAD. The main advantage of this technique over previous ones is that, because the algorithm only need that ADCs are ordered basing on their time mismatches, the absolute accuracy of the mismatch identification method does not matter and, therefore, the requirements on the timing mismatch identification are very low. In addition to that, this correction system uses very simple algorithms able to correct not only for time but also for gain and offset mismatches

Effects of Tourism on Venice: Commercial Changes over 30 Years

Tourism is becoming one of the most important economic drivers in the urban context. With this in mind, several cities have tried to adapt their economies to satisfy the demands of the influx of tourism. The main consequences of this trend are the re-shaping of urban areas, with particular regard to art cities. This phenomenon is particularly evident in Venice’s historical city centre. In order to better comprehend the changes that have taken place, we have put together a research based analysis of the commercial structure of the city. Particular attention has been given to comparing and contrasting the retail business over the last thirty years.commercial structure, historical city centre, retail, Venice

Estimating Koopman operators for nonlinear dynamical systems: a nonparametric approach

The Koopman operator is a mathematical tool that allows for a linear description of non-linear systems, but working in infinite dimensional spaces. Dynamic Mode Decomposition and Extended Dynamic Mode Decomposition are amongst the most popular finite dimensional approximation. In this paper we capture their core essence as a dual version of the same framework, incorporating them into the Kernel framework. To do so, we leverage the RKHS as a suitable space for learning the Koopman dynamics, thanks to its intrinsic finite-dimensional nature, shaped by data. We finally establish a strong link between kernel methods and Koopman operators, leading to the estimation of the latter through Kernel functions. We provide also simulations for comparison with standard procedures.Comment: Pre-print submitted for 19th IFAC Symposium, System Identification: learning models for decision and contro

Perception of interactive vibrotactile cues on the acoustic grand and upright piano

An experiment has been conducted, measuring pianists’ sensitivity to piano key vibrations at the fingers while playing an upright or a grand Yamaha Disklavier piano. At each trial, which consisted in playing loud and long A notes across the whole keyboard, vibrations were either present or absent through setting the Disklavier pianos to normal or quiet mode. Sound feedback was always provided by a MIDI controlled piano synthesizer via isolating ear/headphones, which masked the acoustic sound in normal mode. In partial disagreement with the existing literature, our results suggest that significant vibrotactile cues are produced in the lower range of the piano keyboard, with perceptual cut-off around the middle octave. Possible psychophysical mechanisms supporting the existence of such cues are additionally discussed

A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

In this paper, we consider a wireless network of smart sensors (agents) that monitor a dynamical process and send measurements to a base station that performs global monitoring and decision-making. Smart sensors are equipped with both sensing and computation, and can either send raw measurements or process them prior to transmission. Constrained agent resources raise a fundamental latency-accuracy trade-off. On the one hand, raw measurements are inaccurate but fast to produce. On the other hand, data processing on resource-constrained platforms generates accurate measurements at the cost of non-negligible computation latency. Further, if processed data are also compressed, latency caused by wireless communication might be higher for raw measurements. Hence, it is challenging to decide when and where sensors in the network should transmit raw measurements or leverage time-consuming local processing. To tackle this design problem, we propose a Reinforcement Learning approach to learn an efficient policy that dynamically decides when measurements are to be processed at each sensor. Effectiveness of our proposed approach is validated through a numerical simulation with case study on smart sensing motivated by the Internet of Drones.Comment: 8 pages, 4 figures, submitted to CDC 2022; fixed author name

Methodology for Minimizing Mismatches in Time-Interleaved ADCs

This presentation describes a technique mitigating the impact of timing mismatches in timeinterleaved analog-to-digital converters (ADCs). The systems signal-to-noise and distortion ratio (SINAD) and spurious-free dynamic range (SFDR) are increased by controlling the selection order of the channels ADCs in combination with oversampling and consecutive filtering. The proposed method requires only knowledge of the relative level of timing mismatch between the channel ADCs though not the precise magnitude of the mismatch. The impact of timing mismatch on the SINAD and advanced selection ordering schemes are discussed. Moreover, simulation results are presented comparing the figures of merit of existing techniques

Design of Thermal Management Control Policies for Multiprocessors Systems on Chip

The contribution of this thesis is a thorough study of thermal aware policy design for MPSoCs. The study includes the modelling of their thermal behavior as well as the improvement and the definition of new thermal management and balancing policies. The work is structured on three main specific disciplines. The areas of contributions are: modeling, algorithms and system design. This thesis extends the field of modeling by proposing new techniques to represent the thermal behavior of MPSoCs using a mathematical formalization. Heat transfer and modelling of physical properties of MPSoCs have been extensively studied. Special emphasis is given to the way the system cools down (i.e. micro-cooling, natural heat dissipation etc.) and the heat propagates inside the MPSoC. The second contribution of this work is related to policies, which manage MPSoC working frequencies and micro-cooling pumps to satisfy user requirements in the most effective possible way, while consuming the lowest possible amount of resources. Several families of thermal policies algorithms have been studied and analyzed in this work for both 2D and 3D MPSoCs including liquid cooling technologies. The discipline of system design has also been extended during the development of this thesis. Thermal management policies have been implemented in real emulation platforms and contributions in this area are related to the design and implementation of proposed innovations in real MPSoC platforms

To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

We consider a network of smart sensors for edge computing application that sample a signal of interest and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at the edge generate a fundamental latency-accuracy trade-off: raw measurements are inaccurate but timely, whereas accurate processed updates are available after computational delay. Also, if sensor on-board processing entails data compression, latency caused by wireless communication might be higher for raw measurements. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize overall network performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds computation and communication delays, and propose a Reinforcement Learning-based approach to dynamically allocate computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical simulations with case studies motivated by the Internet of Drones and self-driving vehicles.Comment: 14 pages, 14 figures; submitted to IEEE TNSM; revised versio

Diazo transfer for azido-functional surfaces

Preparation of azido-functionalized polymers is gaining increasing attention. We wish to report an innovative, novel strategy for azido functionalization of polymeric materials, coupling plasma technology and solution processed diazo transfer reactions. This novel approach allows the azido group to be introduced downstream of the material preparation, thus preserving its physicochemical and mechanical characteristics, which can be tailored a priori according to the desired application. The whole process involves the surface plasma functionalization of a material with primary amino groups, followed by a diazo transfer reaction, which converts the amino functionalities into azido groups that can be exploited for further chemoselective reactions. The diazo transfer reaction is performed in a heterogeneous phase, where the azido group donor is in solution. Chemical reactivity of the azido functionalities was verified by subsequent copper-catalyzed azide-alkyne cycloaddition