Rate-Distortion Classification for Self-Tuning IoT Networks

Many future wireless sensor networks and the Internet of Things are expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the signal statistics. New protocols will be then injected as a software as certain events occur. For instance, new data compressors could be (re)programmed on-the-fly as the monitored signal type or its statistical properties change. We consider a lossy compression scenario, where the application tolerates some distortion of the gathered signal in return for improved energy efficiency. To reap the full benefits of this paradigm, we discuss an automatic sensor profiling approach where the signal class, and in particular the corresponding rate-distortion curve, is automatically assessed using machine learning tools (namely, support vector machines and neural networks). We show that this curve can be reliably estimated on-the-fly through the computation of a small number (from ten to twenty) of statistical features on time windows of a few hundreds samples

Of Men, Roles and Rules: Nanni Moretti’s Habemus Papam

This paper focuses on Nanni Moretti’s Habemus Papam and in particular on its representation of the interaction between religion and masculinity. In the light of gender studies, it asks which idea of masculinity, but also of fatherhood, Catholicism and its system of authority tend to encourage according to the film, and it assesses the opportunities for change that the film imaginatively explores. The analysis of the idea of masculinity investigates in particular the distinction between person and office, the necessity of which is dramatically illustrated in the film

SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes

In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems including these devices. The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions. Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics. Our tool has been developed using Matlab(TM) and is available under the GPL license at[1].Comment: Submitted to IEEE EnergyCon 201

screening piety invoking fervour the strange case of italy s televised mass

This paper discusses the television broadcasting of Catholic Masses in Italy today from an interdisciplinary perspective that integrates theology with religion and media studies as well as television studies. After a brief overview of the history of television broadcasting of the Mass and a discussion of its rapid theological acceptance, the paper analyzes the unique success and "proliferation" of televised Masses in Italy. Looking at some of the common characteristics of televised Masses across Italian broadcasting channels, the paper concludes with a reflection on the specificity of (televised) Mass as a ritual action

Compression vs Transmission Tradeoffs for Energy Harvesting Sensor Networks

The operation of Energy Harvesting Wireless Sensor Networks (EHWSNs) is a very lively area of research. This is due to the increasing inclination toward green systems, in order to reduce the energy consumption of human activities at large and to the desire of designing networks that can last unattended indefinitely (see, e.g., the nodes employed in Wireless Sensor Networks, WSNs). Notably, despite recent technological advances, batteries are expected to last for less than ten years for many applications and their replacement is often prohibitively expensive. This problem is particularly severe for urban sensing applications, think of, e.g., sensors placed below the street level to sense the presence of cars in parking lots, where the installation of new power cables is impractical. Other examples include body sensor networks or WSNs deployed in remote geographic areas. In contrast, EHWNs powered by energy scavenging devices (renewable power) provide potentially maintenance-free perpetual network operation, which is particularly appealing, especially for highly pervasive Internet of Things. Lossy temporal compression has been widely recognized as key for Energy Constrained Wireless Sensor Networks (WSN), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. The first part of this thesis deals with the evaluation of a number of lossy compression methods from the literature, and the analysis of their performance in terms of compression efficiency, computational complexity and energy consumption. Specifically, as a first step, a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and Wavelet-based models is carried out, by looking at their performance as a function of relevant signal statistics. After that, closed form expressions for their overall energy consumption and signal representation accuracy are obtained through numerical fittings. Lastly, the benefits that lossy compression methods bring about in interference-limited multi-hop networks are evaluated. In this scenario the channel access is a source of inefficiency due to collisions and transmission scheduling. The results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay and increased reliability performance. The subsequent part of the thesis copes with the problem of energy management for EHWSNs where sensor batteries are recharged via the energy harvested through a solar panel and sensors can choose to compress data before transmission. A scenario where a single node communicates with a single receiver is considered. The task of the node is to periodically sense some physical signal and report the measurements to the receiver (sink). We assume that this task is delay tolerant, i.e., the sensor can store a certain number of measurements in the memory buffer and send one or more packets of data after some time. Since most physical signals exhibit strong temporal correlation, the data in the buffer can often be compressed by means of a lossy compression method in order to reduce the amount of data to be sent. Lossy compression schemes allow us to select the compression ratio and trade some accuracy in the data reconstruction at the receiver for more energy savings at the transmitter. Specifically, our objective is to obtain the policy, i.e., the set of decision rules that describe the node behavior, that jointly maximizes throughput and reconstruction fidelity at the sink while meeting some predefined energy constraints, e.g., the battery charge level should never go below a guard threshold. To obtain this policy, the system is modeled as a Constrained Markov Decision Process (CMDP), and solved through Lagrangian Relaxation and Value Iteration Algorithm. The optimal policies are then compared with heuristic policies in different energy budget scenarios. Moreover the impact of the delay on the knowledge of the Channel State Information is investigated. Two more parts of this thesis deal with the development of models for the generation of space-time correlated signals and for the description of the energy harvested by outdoor photovoltaic panels. The former are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering or compression algorithms. The latter are especially useful in the investigation and in the optimization of EHWSNs. These models will be presented at the beginning and then intensively used for the analysis and the performance evaluation of the schemes that are treated in the remainder of the thesis

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

SURF: Subject-Adaptive Unsupervised ECG Signal Compression for Wearable Fitness Monitors

Recent advances in wearable devices allow non-invasive and inexpensive collection of biomedical signals including electrocardiogram (ECG), blood pressure, respiration, among others. Collection and processing of various biomarkers are expected to facilitate preventive healthcare through personalized medical applications. Since wearables are based on size- and resource-constrained hardware, and are battery operated, they need to run lightweight algorithms to efficiently manage energy and memory. To accomplish this goal, this paper proposes SURF, a subject-adaptive unsupervised signal compressor for wearable fitness monitors. The core idea is to perform a specialized lossy compression algorithm on the ECG signal at the source (wearable device), to decrease the energy consumption required for wireless transmission and thus prolong the battery lifetime. SURF leverages unsupervised learning techniques to build and maintain, at runtime, a subject-adaptive dictionary without requiring any prior information on the signal. Dictionaries are constructed within a suitable feature space, allowing the addition and removal of code words according to the signal's dynamics (for given target fidelity and energy consumption objectives). Extensive performance evaluation results, obtained with reference ECG traces and with our own measurements from a commercial wearable wireless monitor, show the superiority of SURF against state-of-the-art techniques, including: 1) compression ratios up to 90-times; 2) reconstruction errors between 2% and 7% of the signal's range (depending on the amount of compression sought); and 3) reduction in energy consumption of up to two orders of magnitude with respect to sending the signal uncompressed, while preserving its morphology. SURF, with artifact prone ECG signals, allows for typical compression efficiencies (CE) in the range $\text {CE} \in [{40,50}]$ , which means that the data rate of 3 kbit/s that would be required to send the uncompressed ECG trace is lowered to 60 and 75 bit/s for CE = 40 and CE = 50, respectively

Hydropower plants: pride and prejudice

The development of alternative energy solutions to meet the increasing energy demand requires the expansion of the production network. In this context hydropower plants (HPPs) represent a reliable renewable energy source [3] and the possibility of integrating a pumping storage system makes HPPs an excellent way to stock energy. Besides energy generation, hydropower plants present numerous benefits, including flood control and water supply, leisure and storage of electricity [7]. Nevertheless in the recent years some criticism was raised against megaprojects and few studies pointed out that budgets are constantly exceeded and schedules overrun. The methodology proposed by Gomez and Probst [4] is hereafter used allowing to study the hydropower cost evaluation as a complex problem and to reveal the key factors that have a strong influence. The purpose is to identify the processes which leads to an increase of the final cost of large dam project. Results clearly showed the involvement of uncontrollable factors in the process. The reasoning leads to a final statement: the human impossibility to foresee the unpredictable does not justify the criticism and the prejudice that was risen against hydropower plants, whose benefits remain undeniable

Validation of the T-Lymphocyte Subset Index (TLSI) as a Score to Predict Mortality in Unvaccinated Hospitalized COVID-19 Patients

Lymphopenia has been consistently reported as associated with severe coronavirus disease 2019 (COVID-19). Several studies have described a profound decline in all T-cell subtypes in hospitalized patients with severe and critical COVID-19. The aim of this study was to assess the role of T-lymphocyte subset absolute counts measured at ward admission in predicting 30-day mortality in COVID-19 hospitalized patients, validating a new prognostic score, the T-Lymphocyte Subset Index (TLSI, range 0–2), based on the number of T-cell subset (CD4+ and CD8+) absolute counts that are below prespecified cutoffs. These cutoff values derive from a previously published work of our research group at Policlinico Tor Vergata, Rome, Italy: CD3+CD4+ < 369 cells/µL, CD3+CD8+ < 194 cells/µL. In the present single-center retrospective study, T-cell subsets were assessed on admission to the infectious diseases ward. Statistical analysis was performed using JASP (Version 0.16.2. JASP Team, 2022, The Amsterdam, The Netherlands) and Prism8 (version 8.2.1. GraphPad Software, San Diego, CA, USA). Clinical and laboratory parameters of 296 adult patients hospitalized because of COVID-19 were analyzed. The overall mortality rate was 22.3% (66/296). Survivors (S) had a statistically significant lower TLSI score compared to non-survivors (NS) (p < 0.001). Patients with increasing TLSI scores had proportionally higher rates of 30-day mortality (p < 0.0001). In the multivariable logistic analysis, the TLSI was an independent predictor of in-hospital 30-day mortality (OR: 1.893, p = 0.003). Survival analysis showed that patients with a TLSI > 0 had an increased risk of death compared to patients with a TLSI = 0 (hazard ratio: 2.83, p < 0.0001). The TLSI was confirmed as an early and independent predictor of COVID-19 in-hospital 30-day mortalit