Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

Deducing the structure of neural circuits is one of the central problems of modern neuroscience. Recently-introduced calcium fluorescent imaging methods permit experimentalists to observe network activity in large populations of neurons, but these techniques provide only indirect observations of neural spike trains, with limited time resolution and signal quality. In this work we present a Bayesian approach for inferring neural circuitry given this type of imaging data. We model the network activity in terms of a collection of coupled hidden Markov chains, with each chain corresponding to a single neuron in the network and the coupling between the chains reflecting the network's connectivity matrix. We derive a Monte Carlo Expectation--Maximization algorithm for fitting the model parameters; to obtain the sufficient statistics in a computationally-efficient manner, we introduce a specialized blockwise-Gibbs algorithm for sampling from the joint activity of all observed neurons given the observed fluorescence data. We perform large-scale simulations of randomly connected neuronal networks with biophysically realistic parameters and find that the proposed methods can accurately infer the connectivity in these networks given reasonable experimental and computational constraints. In addition, the estimation accuracy may be improved significantly by incorporating prior knowledge about the sparseness of connectivity in the network, via standard L$_1$ penalization methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Design & Characterization of resistance-based corrosion under-insulation sensor

Corrosion under insulation or composite wrappings has become a major concern in aging petrochemical plants. It can produce unseen pits and cracks that could lead to catastrophic failures with hours of downtime and millions of ringgits in losses. There are available sensors designed to detect this type of corrosion, however, it cannot give the actual metal loss as required by the engineers. In this work, a resistance-based corrosion sensor is proposed for this purpose. The sensor is going to be designed and developed from iron (Fe) compound thin films. This sensor is going to be integrated into a wireless system consisting of a transponder and integration. The sensor design requirement is to have a resistance of 200 Ω to 2000 Ω. The shape and features may vary depending on the requirement of the transponder. The sensor is fabricated using thermal evaporator and PCB technology with the calculated dimension of connector and finger, the resistance obtained is 1863.69 Ω

Sparse component separation for accurate CMB map estimation

The Cosmological Microwave Background (CMB) is of premier importance for the cosmologists to study the birth of our universe. Unfortunately, most CMB experiments such as COBE, WMAP or Planck do not provide a direct measure of the cosmological signal; CMB is mixed up with galactic foregrounds and point sources. For the sake of scientific exploitation, measuring the CMB requires extracting several different astrophysical components (CMB, Sunyaev-Zel'dovich clusters, galactic dust) form multi-wavelength observations. Mathematically speaking, the problem of disentangling the CMB map from the galactic foregrounds amounts to a component or source separation problem. In the field of CMB studies, a very large range of source separation methods have been applied which all differ from each other in the way they model the data and the criteria they rely on to separate components. Two main difficulties are i) the instrument's beam varies across frequencies and ii) the emission laws of most astrophysical components vary across pixels. This paper aims at introducing a very accurate modeling of CMB data, based on sparsity, accounting for beams variability across frequencies as well as spatial variations of the components' spectral characteristics. Based on this new sparse modeling of the data, a sparsity-based component separation method coined Local-Generalized Morphological Component Analysis (L-GMCA) is described. Extensive numerical experiments have been carried out with simulated Planck data. These experiments show the high efficiency of the proposed component separation methods to estimate a clean CMB map with a very low foreground contamination, which makes L-GMCA of prime interest for CMB studies.Comment: submitted to A&

Pedestrain Monitoring System Using Wi-Fi Technology and RSSI Based Localization

This paper presentsa new simple mobile tracking system based on IEEE802.11 wireless signal detection, which can be used for analyzingthe movement of pedestrian traffic. Wi-Fi packets emitted by Wi-Fi enabled smartphones are received at a monitoring station and these packets contain date, time, MAC address, and other information. The packets are received at a number of stations, distributed throughout the monitoring zone, which can measure the received signal strength. Based on the location of stations and data collected at the stations, the movement of pedestrian traffic can be analyzed. This information can be used to improve the services, such as better bus schedule time and better pavement design. In addition, this paper presents a signal strength based localization method

Context-aware Assessment Using QR-codes

In this paper we present the implementation of a general mechanism to deliver tests based on mobile devices and matrix codes. The system is an extension of Siette, and has not been specifically developed for any subject matter. To evaluate the performance of the system and show some of its capabilities, we have developed a test for a second-year college course on Botany at the School of Forestry Engineering. Students were equipped with iPads and took an outdoor test on plant species identification. All students were able to take and complete the test in a reasonable time. Opinions expressed anonymously by the students in a survey about the usability of the system and the usefulness of the test were very favorable. We think that the application presented in this paper can broaden the applicability of automatic assessment techniques.The presentation of this work has been co-founded by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Technology needs assessment of an atmospheric observation system for tropospheric research missions, part 1

The technology advancements needed to implement the atmospheric observation satellite systems for air quality research were identified. Tropospheric measurements are considered. The measurements and sensors are based on a model of knowledge objectives in atmospheric science. A set of potential missions and attendant spacecraft and sensors is postulated. The results show that the predominant technology needs will be in passive and active sensors for accurate and frequent global measurements of trace gas concentration profiles