The new york city covid‐19 spread in the 2020 spring: A study on the potential role of particulate using time series analysis and machine learning

This study investigates the potential association between the daily distribution of the PM2,5 air pollutant and the initial spreading of COVID‐19 in New York City. We study the period from 4 March to 22 March 2020, and apply our analysis to all five counties, including the city, plus seven neighboring counties, including both urban and peripheral districts. Using the Granger causality methodology, and considering the maximum lag period (14 days) between infection and the correspondent diagnosis, we found that the time series of the new daily infections registered in those 12 counties appear to correlate to the time series of the concentrations of the PM2.5 particulate circulating in the air, with 33 over 36 statistical tests with a p‐value less than 0.005, thus confirming such a hypothesis. Moreover, looking for further confirmation of this association, we train four different machine learning algorithms on a portion of those time series. These are able to predict that the number of the new daily infections would have surpassed a given infections threshold for the remaining portion of the series, with an average accuracy ranging from 84% to 95%, depending on the algorithm and/or on the specific county under observation. This is similar to other results obtained from several polluted urban areas, e.g., Wuhan, Xiaogan, and Huanggang in China, and Northern Italy. Our study provides further evidence that ambient air pollutants can be associated with a daily COVID‐19 infection incidence

Unsupervised hyperspectral image segmentation of films: a hierarchical clustering-based approach

Hyperspectral imaging (HSI) has been drastically applied in recent years to cultural heritage (CH) analysis, conservation, and also digital restoration. However, the efficient processing of the large datasets registered remains challenging and still in development. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative machine learning approach to the most common practices, such as principal component analysis(PCA). HCA has shown its potential in the past decades for spectral data classification and segmentation in many other fields, maximizing the information to be extracted from the high-dimensional spectral dataset via the formation of the agglomerative hierarchical tree. However, to date, there has been very limited implementation of HCA in the field of cultural heritage. Data used in this experiment were acquired on real historic film samples with various degradation degrees, using a custom-made push-broom VNIR hyperspectral camera (380–780nm). With the proposed HCA workflow, multiple samples in the entire dataset were processed simultaneously and the degradation areas with distinctive characteristics were successfully segmented into clusters with various hierarchies. A range of algorithmic parameters was tested, including the grid sizes, metrics, and agglomeration methods, and the best combinations were proposed at the end. This novel application of the semi-automating and unsupervised HCA could provide a basis for future digital unfading, and show the potential to solve other CH problems such as pigment mapping

Performance function for time-jittered equispaced sampling wattmeters

This paper evaluates the effect of time-jitter in the equally spaced sampling wattmeters on the hypothesis of equal effects in the two channels and a jitter uncorrelated with the input signals. It is shown that time-jitter, which is a random fluctuation with respect to the nominal sampling time, introduces a frequency limitation which is evaluated together with that due to the sampling strategy and filtering algorithm. The theoretical results are compared with the simulated one

The effect of time-jitter in equispaced sampling wattmeters

This paper evaluates the effect of time-jitters in the equally spaced sampling wattmeters on the hypothesis of jitters uncorrelated with the input signals. The general case of two distinct time-jitters is considered, one common to the two channels and the other different for each one of them. The performance of the wattmeter has been evaluated by considering the asymptotic statistic parameters of the output. It has been shown that the different time-jitters introduce a bias and that both time-jitters contribute to the variance of the output. In any case, time-jitters introduce further bandwidth limitations which must be taken into account in the wattmeter accuracy evaluation. The theoretical results have been compared with simulated and experimental findings. Experimental results were obtained with a prototype in which both common and different time-jitters were separately added to the equally spaced sampling instants of the two input channels. In both cases, all the results were in good agreement with theoretical expectation

On supporting university communities in indoor wayfinding: An inclusive design approach

Mobility can be defined as the ability of people to move, live and interact with the space. In this context, indoor mobility, in terms of indoor localization and wayfinding, is a relevant topic due to the challenges it presents, in comparison with outdoor mobility, where GPS is hardly exploited. Knowing how to move in an indoor environment can be crucial for people with disabilities, and in particular for blind users, but it can provide several advantages also to any person who is moving in an unfamiliar place. Following this line of thought, we employed an inclusive by design approach to implement and deploy a system that comprises an Internet of Things infrastructure and an accessible mobile application to provide wayfinding functions, targeting the University community. As a real word case study, we considered the University of Bologna, designing a system able to be deployed in buildings with different configurations and settings, considering also historical buildings. The final system has been evaluated in three different scenarios, considering three different target audiences (18 users in total): i. students with disabilities (i.e., visual and mobility impairments); ii. campus students; and iii. visitors and tourists. Results reveal that all the participants enjoyed the provided functions and the indoor localization strategy was fine enough to provide a good wayfinding experience

Implementation and performance evaluation of a broadband digital harmonic vector voltmeter

A broadband digital harmonic vector voltmeter proposed previously and studied theoretically by the authors was implemented using a special-purpose, random sampling strategy, to avoid the bandwidth limitations due to the finite conversion time of the sample-and-hold and analog-to-digital-conversion (S/H-ADC) devices. The experimental results have shown that the bandwidth of the instrument is not limited by the finite conversion time of S/H-ADC devices, since good accuracy can be achieved even when the average sampling frequency is much lower than the signal bandwidth. The amplitude and phase uncertainty, with sinusoidal test signals up to 1 MHz and an average sampling rate of 10 kHz, was found to be lower than 3% and 0.03 rad, respectively. For more careful testing of the broadband performance of our instrument, we also carried out two-frequency, variable order harmonic measurements, which showed good accuracy (amplitude error less than 1.5% and phase error less than 0.03 rad) with harmonics up to 300 kHz. Reasonable accuracy (i.e., sufficient to correctly reconstruct the actual signal waveform) was also found with a highly distorted square-wave signa

Recursive random-sampling strategy for a digital wattmeter

A recursive random-sampling strategy is proposed for the implementation of a digital broadband wattmeter. In this strategy each sampling instant is obtained by adding to the preceding one a predetermined constant lag plus a random increment. In order to correlate the measurement uncertainty to the bandwidth, the asymptotic mean-square error arising from the sampling strategy and the filtering algorithm is evaluated and analyzed; it has been shown that the proposed sampling strategy does not limit the bandwidth of the instrument if an appropriate statistical distribution of the random increments is selected. The theoretical results are compared with those obtained by simulating the measurement proces

Raveguard: A noise monitoring platform using low-end microphones and machine learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time\u2013space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring