Crowdsensing the Speaker Count in the Wild: Implications and Applications

Abstract-The Mobile Crowd Sensing (MCS) paradigm enables large-scale sensing opportunities at lower deployment costs than dedicated infrastructures by utilizing the large number of today's mobile devices. In the context of MCS, end users with sensing and computing devices can share and extract information of common interest. In this article, we examine Crowd++, a MCS application, which accurately estimates the number of people talking in a certain place through unsupervised machine learning analysis on audio segments captured by mobile devices. Such a technique can find application in many domains, such as crowd estimation, social sensing, and personal well-being assessment. In this article, we demonstrate the utility of this technique in the context of conference room usage estimation, social diary, and social engagement in a power efficient manner followed by a discussion on privacy and possible optimizations to Crowd++ software

Slope Failure Risk Assessment Considering Both the Randomness of Groundwater Level and Soil Shear Strength Parameters

Conducting research on slope failure risk assessment is beneficial for the sustainable development of slopes. There will be various failure modes considering both the randomness of the groundwater level and soil shear strength parameters. Based on the integrated failure probability (IFP), the traditional failure risk analysis needs to count all failure modes, including the failure probability (Pf) and failure risk coefficient (C), one-by-one. A new slope failure risk assessment method that uses the sum of the element failure risk to calculate the overall failure risk is proposed in this paper and considers both the randomness of the groundwater level and soil shear strength parameters. The element failure probability is determined by their location information and failure situation; the element failure risk coefficient is determined by their area. It transforms the complex overall failure risk problem into a simple element failure risk problem, which simplifies the calculation process and improves the calculation efficiency greatly. The correctness is verified with the systematic analysis of a classical case. The results show that the slope failure probability and failure risk are greatly increased from 1.40% to 3.30% and 0.829 m2 to 2.094 m2 with rising groundwater level, respectively

Lower Bound Limit Analysis of Non-Persistent Jointed Rock Masses Using Mixed Numerical Discretization

The bearing capacity of a non-persistent jointed rock mass containing a rock bridge is investigated by combining the lower bound limit analysis theory, a mixed numerical discrete method, and linear mathematical programming. A mixed numerical discrete method is proposed to divide non-persistent jointed rock masses in which rigid block elements are used to simulate the rock blocks, whereas the finite element method is used to simulate the intact rock bridges. A linear mathematical programming model for the ultimate bearing capacity is constructed and solved using the interior point algorithm. The proposed formulation is validated by application to three rock slopes

Lower Bound Limit Analysis of Non-Persistent Jointed Rock Masses Using Mixed Numerical Discretization

The bearing capacity of a non-persistent jointed rock mass containing a rock bridge is investigated by combining the lower bound limit analysis theory, a mixed numerical discrete method, and linear mathematical programming. A mixed numerical discrete method is proposed to divide non-persistent jointed rock masses in which rigid block elements are used to simulate the rock blocks, whereas the finite element method is used to simulate the intact rock bridges. A linear mathematical programming model for the ultimate bearing capacity is constructed and solved using the interior point algorithm. The proposed formulation is validated by application to three rock slopes

Crowd++: Unsupervised speaker count with smartphones,”

ABSTRACT Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it's possible to accurately estimate the number of people talking in a certain place -with an average error distance of 1.5 speakers -through unsupervised machine learning analysis on audio segments captured by the smartphones. Inference occurs transparently to the user and no human intervention is needed to derive the classification model. Our results are based on the design, implementation, and evaluation of a system called Crowd++, involving 120 participants in 10 very different environments. We show that no dedicated external hardware or cumbersome supervised learning approaches are needed but only off-the-shelf smartphones used in a transparent manner. We believe our findings have profound implications in many research fields, including social sensing and personal wellbeing assessment

Chlorination behaviors for green and efficient vanadium recovery from tailing of refining crude titanium tetrachloride

The refined tailing, generated from refining of titanium tetrachloride (TiCl4) for vanadium (V) removal, is a hazardous material to environment due to the high content of V. Aiming at effective and selective extraction of V from the refined tailing, a fluidized chlorination process was proposed in present work. The chlorination behaviors of the refined tailing which determine the efficiency and selectivity of V extraction were emphatically investigated. A resultant 96.36% of V and 4.23% of Ti can be synchronously extracted from the tailing at the optimum conditions of 800 degrees C for 60 min, with the pressure fraction of chlorine [P(Cl-2)/P(Cl-2 +N-2)1 = 0.5 and the mass fraction of petroleum coke in raw materials for chlorination at 10 wt%. High purity vanadium oxytrichloride (VOCl3, higher than 99.99 wt%) can be finally obtained via further simple purification of the collected chloride product. Moreover, the chlorination residue containing concentrated TiO2 has the potential to be further utilized for Ti extraction. Thus the process provides a new prospect for effective, clean and comprehensive utilization of the refined tailing, which can solve the hazardous waste recycle and environmental concerns simultaneously

Structuring β‑Ga₂O₃ Photonic Crystal Photocatalyst for Efficient Degradation of Organic Pollutants

Coupling photocatalysts with photonic crystals structure is based on the unique property of photonic crystals in confining, controlling, and manipulating the incident photons. This combination enhances the light absorption in photocatalysts and thus greatly improves their photocatalytic performance. In this study, Ga₂O₃ photonic crystals with well-arranged skeleton structures were prepared <i>via</i> a dip-coating infiltration method. The positions of the electronic band absorption for Ga₂O₃ photonic crystals could be made to locate on the red edge, on the blue edge, and away from the edge of their photonic band gaps by changing the pore sizes of the samples, respectively. Particularly, the electronic band absorption of the Ga₂O₃ photonic crystal with a pore size of 135 nm was enhanced more than other samples by making it locate on the red edge of its photonic band gap, which was confirmed by the higher instantaneous photocurrent and photocatalytic activity for the degradation of various organic pollutants under ultraviolet light irradiation. Furthermore, the degradation mechanism over Ga₂O₃ photonic crystals was discussed. The design of Ga₂O₃ photonic crystals presents a prospective application of photonic crystals in photocatalysis to address light harvesting and quantum efficiency problems through manipulating photons or constructing photonic crystal structure as groundwork