1,489 research outputs found
Life and Action in Ethics and in Politics
No full textThe symposium on Michael Thompson's "Life and Action, is organized according to the threefold
partition of the book. As for part one, Paolo Costa focuses on
the logical and metaphysical understanding of “life-form” and
relates it to similar approaches in philosophical anthropology. As
for part two, Constantine Sandis examines the role of simple past
and progressive tenses in the naïve theory of action and contrasts
it with alternative contemporary approaches in action theory.
Matteo Bianchin questions Thompson’s rejection of folk
psychological accounts by focusing on phenomenal intentionality
and action planning. As for part three, Arto Laitinen considers
Thompson’s understanding of practices as a source of goodness
in the light of the Hegelian distinction between Moralität und
Sittlichkeit. Italo Testa discusses Thompson’s anti-individualist
account of dispositions and social practices, and assesses its
relevance for social philosophy and social ontology. Ingrid
Salvatore interrogates Thompson’s understanding of Rawls’s
“Two concepts of Rule” and rule-like practices
APPENDIX_I – Supplemental material for Explicit and implicit experience of own’s body in painful musculoskeletal disorders and rheumatic diseases: A scoping review protocol of available quantitative and qualitative evidence
No full textSupplemental material, APPENDIX_I for Explicit and implicit experience of own’s body in painful musculoskeletal disorders and rheumatic diseases: A scoping review protocol of available quantitative and qualitative evidence by Antonello Viceconti, Eleonora Maria Camerone, Deborah Luzzi, Matteo Pardini, Diego Ristori, Alberto Gallace and Marco Testa in SAGE Open Medicine</p
Stochastic Optimization and Machine Learning Modeling for Wireless Networking
No full textIn the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, including multiple input multiple output architectures or software defined networking: as an example, the next generation of mobile communication, known as 5G, is expected to provide data rates of tens of megabits per second for tens of thousands of users and only 1 ms latency. In order to achieve such demanding performance, these systems need to effectively model the considerable level of uncertainty related to fading transmission channels, interference, or the presence of noise in the data.
In this thesis, we will present how different approaches can be adopted to model these kinds of scenarios, focusing on wireless networking applications. In particular, the first part of this work will show how stochastic optimization models can be exploited to design energy management policies for wireless sensor networks. Traditionally, transmission policies are designed to reduce the total amount of energy drawn from the batteries of the devices; here, we consider energy harvesting wireless sensor networks, in which each device is able to scavenge energy from the environment and charge its battery with it. In this case, the goal of the optimal transmission policies is to efficiently manage the energy harvested from the environment, avoiding both energy outage (i.e., no residual energy in a battery) and energy overflow (i.e., the impossibility to store scavenged energy when the battery is already full).
In the second part of this work, we will explore the adoption of machine learning techniques to tackle a number of common wireless networking problems. These algorithms are able to learn from and make predictions on data, avoiding the need to follow limited static program instructions: models are built from sample inputs, thus allowing for data-driven predictions and decisions. In particular, we will first design an on-the-fly prediction algorithm for the expected time of arrival related to WiFi transmissions. This predictor only exploits those network parameters available at each receiving node and does not require additional knowledge from the transmitter, hence it can be deployed without modifying existing standard transmission protocols. Secondly, we will investigate the usage of particular neural network instances known as autoencoders for the compression of biosignals, such as electrocardiography and photo plethysmographic sequences. A lightweight lossy compressor will be designed, able to be deployed in wearable battery-equipped devices with limited computational power. Thirdly, we will propose a predictor for the long-term channel gain in a wireless network. Differently from other works in the literature, such predictor will only exploit past channel samples, without resorting to additional information such as GPS data. An accurate estimation of this gain would enable to, e.g., efficiently allocate resources and foretell future handover procedures. Finally, although not strictly related to wireless networking scenarios, we will show how deep learning techniques can be applied to the field of autonomous driving. This final section will deal with state-of-the-art machine learning solutions, proving how these techniques are able to considerably overcome the performance given by traditional approaches
Autoregressive process parameters estimation from Compressed Sensing measurements and Bayesian dictionary learning
No full textThe main contribution of this thesis is the introduction of new techniques which allow to perform signal processing operations on signals represented by means of compressed sensing. Exploiting autoregressive modeling of the original signal, we obtain a compact yet representative description of the signal which can be estimated directly in the compressed domain. This is the key concept on which the applications we introduce rely on. In fact, thanks to proposed the framework it is possible to gain information about the original signal given compressed sensing measurements. This is done by means of autoregressive modeling which can be used to describe a signal through a small number of parameters. We develop a method to estimate these parameters given the compressed measurements by using an ad-hoc sensing matrix design and two different coupled estimators that can be used in different scenarios. This enables centralized and distributed estimation of the covariance matrix of a process given the compressed sensing measurements in a efficient way at low communication cost. Next, we use the characterization of the original signal done by means of few autoregressive parameters to improve compressive imaging. In particular, we use these parameters as a proxy to estimate the complexity of a block of a given image. This allows us to introduce a novel compressive imaging system in which the number of allocated measurements is adapted for each block depending on its complexity, i.e., spatial smoothness. The result is that a careful allocation of the measurements, improves the recovery process by reaching higher recovery quality at the same compression ratio in comparison to state-of-the-art compressive image recovery techniques. Interestingly, the parameters we are able to estimate directly in the compressed domain not only can improve the recovery but can also be used as feature vectors for classification. In fact, we also propose to use these parameters as more general feature vectors which allow to perform classification in the compressed domain. Remarkably, this method reaches high classification performance which is comparable with that obtained in the original domain, but with a lower cost in terms of dataset storage. In the second part of this work, we focus on sparse representations. In fact, a better sparsifying dictionary can improve the Compressed Sensing recovery performance. At first, we focus on the original domain and hence no dimensionality reduction by means of Compressed Sensing is considered. In particular, we develop a Bayesian technique which, in a fully automated fashion, performs dictionary learning. More in detail, using the uncertainties coming from atoms selection in the sparse representation step, this technique outperforms state-of-the-art dictionary learning techniques. Then, we also address image denoising and inpainting tasks using the aforementioned technique with excellent results. Next, we move to the compressed domain where a better dictionary is expected to provide improved recovery. We show how the Bayesian dictionary learning model can be adapted to the compressive case and the necessary assumptions that must be made when considering random projections. Lastly, numerical experiments confirm the superiority of this technique when compared to other compressive dictionary learning technique
Compressive Bayesian K-SVD
No full textCompressed Sensing (CS) is an established way to perform efficient dimensionality reduction during a signal’s acquisition process. However, the common transform bases used in CS to represent a signal often lead to a compressible representation that is not optimal in terms of compactness. In this paper we present a novel dictionary learning algorithm designed to work with CS data. Following our approach, dictionaries learned directly from the signal’s random projections are specifically suited to the signal class of interest, resulting in very sparse representations. Moreover, since the proposed method lays its foundation in a Bayesian dictionary learning algorithm, no prior information such as the signals’ sparsity is needed because it is inferred directly from the data. We show the superiority of our approach by comparing it with a state-of-the-art CS dictionary learning algorithm
Autoregressive Process Parameter Estimation from Compressed Sensing Measurements
No full textIn this paper we introduce a least squares estimator of the regression coefficients of an autoregressive process acquired by means of Compressed Sensing (CS). Unlike common CS problems in which we only know that the signal is sparse, using the proposed autoregressive model we can gain knowledge about the structure of the original signal without recovering it. This problem is addressed by introducing an ad-hoc sensing matrix able to preserve the structure of the regression. We numerically validate the performance of this matrix. Moreover, we present applications that naturally exploit this additional information we can directly obtain from the compressed data, and particularly power spectral density estimation from CS measurement
Distributed covariance estimation for compressive signal processing
No full textIn this paper we present a novel technique for the distributed estimation of the covariance matrix of an additive colored noise process affecting Compressed Sensing (CS) measurements. The main application is in wireless sensor networks, where nodes sense signals in CS format in order to save energy in the computation and transmission stages. The proposed technique enables a variety of compressive signal processing operations to be performed at each node directly on the linear measurements, such as detection, exploiting the knowledge of the noise statistics, thereby achieving improved performance. The parametric approach we introduce promises to yield good results while keeping the communication cost low. Hence, we validate our technique by evaluating the error on the estimated covariance matrix, and by including it in a compressive detection tas
Compressive estimation and imaging based on autoregressive models
No full textCompressed sensing (CS) is a fast and efficient way to obtain compact signal representations. Oftentimes, one wishes to extract some information from the available com- pressed signal. Since CS signal recovery is typically expensive from a computational point of view, it is inconvenient to first recover the signal and then extract the information. A much more effective approach consists in estimating the information directly from the signal's linear measurements. In this paper, we propose a novel framework for compressive estimation of autoregressive (AR) process parameters based on ad hoc sensing matrix construction. More in detail, we introduce a compressive least square estimator for AR(p) parameters and a specific AR(1) compressive Bayesian estimator. We exploit the proposed techniques to address two important practical problems. The first is compressive covariance estimation for Toeplitz structured covariance matrices where we tackle the problem with a novel parametric approach based on the estimated AR parameters. The second is a block-based compressive imaging system, where we introduce an algorithm that adaptively calculates the number of measurements to be acquired for each block from a set of initial measurements based on its degree of compressibility. We show that the proposed techniques outperform the state-of- the-art methods for these two problems
Cement-Based Radiative Coolers for Photovoltaics: Towards a Practical Design
No full textResearch conducted in the framework of MIRACLE Project (Photonic Metaconcrete with Infrared RAdiative Cooling capacity for Large Energy savings, GA 964450), coordinated by Dr. Jorge Sánchez Dolado, from Centro de Física de Materiales (CFM).In 2014, the experimental realization of radiative coolers capable of reaching sub-ambient temperatures
under direct sunlight has opened up new possibilities for the thermal management of solar cells. Radiative
coolers eject excess heat by emitting thermal radiation within the so-called atmosphere transparency
window. The completely passive nature of this process and its reliance on material properties only, make
radiative coolers extremely attractive in terms of energy efficiency. Integrated with a photovoltaic cell, the
radiative cooler can reduce the cell operating temperature, leading to high efficiency and lifetime gains.
Yet, most radiative coolers in the literature are metamaterials with scarce elements or complex fabrications
processes, or organic materials with potential UV instability, with questionable economic viability or
reliability. To address this problem, we have recently proposed cement-based materials as a low-cost,
scalable and stable solution for photovoltaics cooling, showing that their electromagnetic properties can be
tuned to maximize their thermal emissivity by acting on their microstructure. In particular, using a detailed
balance model, we have demonstrated that their cooling performance could increase the efficiency of silicon
solar cells by up to 9% and extended their lifetime by up to 4 times. In this work, we take a further step
towards the experimental realization of this attractive concept, by investigating possible approaches,
requirements and prospects for the practical design of photovoltaic systems employing cement-based
radiative coolers.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 964450.-- Conference Proceedings of the 16th ICCC, Bangkok, 18-22 September 2023.Peer reviewe
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