Pedestrian Counting Based on Piezoelectric Vibration Sensor

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods.Peer reviewe

Almost sure consensus for multi-agent systems with two level switching

In most literatures on the consensus of multi-agent systems (MASs), the agents considered are time-invariant. However in many cases, for example in airplane formation, the agents have switching dynamics and the connections between them are also changing. This is called two-level switching in this paper. We study almost sure (AS) consensus for a class of two-level switching systems. At the low level of agent dynamics, switching is determin- istic and controllable. The upper level topology switching is random and follows a Markov chain. The transition probability of the Markov chain is not fixed, but varies when low level dynamics changes. For this class of MASs, a sufficient condition for AS consensus is developed in this paper

Pedestrian Counting Based on Piezoelectric Vibration Sensor

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods

Pedestrian Counting Based on Piezoelectric Vibration Sensor

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods.Peer reviewe

SPARC Levels Modulate the Capacity of Mitomycin to Inhibit the Proliferation of Human Tenon’s Capsule Fibroblasts

Purpose. To evaluate the role of SPARC in the antiproliferation effect of MMC on human Tenon’s fibroblasts (HTF). Method. Sixteen PACG patients aged 59 ± 10 years (31–72 years), including 6 males and 10 females, were recruited. Tenon tissue was harvested during filtering surgery. Cell density was evaluated after MMC application with different concentrations and application times, by which the optimized MMC application modality was determined. MMC, si-SPARC, or SPARC protein was used when needed to evaluate the cell densities under different conditions, by which the role of SPARC in MMC-mediated antifibrotic process was identified. Results. Considering that the cell densities, as well as SPARC expression on mRNA and protein levels, are relatively stable when the MMC concentration is higher than 0.02% and exposure time longer than 90 s, we chose the MMC application pattern with 0.02% and 90 s as an optimized pattern for the downstream work. Compared to control, the si-SPARC and MMC downregulated the SPARC protein by 91% (P<0.01) and 65% (P<0.01) and mRNA by 96% (P<0.01) and 64% (P<0.01), respectively. MMC decreases the cell densities by 53.50% compared to control. si-SPARC + MMC dramatically deceased the cell density no matter compared to the control group (P<0.01) or MMC group (P<0.01); correspondingly, the relative collagen gel area in the MMC + si-SPARC group was higher than that in the MMC group or si-SPARC group (P<0.05). The reactive oxygen species expression in the MMC + si-SPARC group is higher than that in the MMC group (P<0.05). Conclusion. This study demonstrates that in HTF, (1) MMC downregulates the expression of SPARC in protein and mRNA levels; (2) SPARC depletion has synergistic effect on the antifibrotic effect of MMC; and (3) reactive oxygen species are the possible mediator in the antifibrotic effect of MMC and si-SPARC

Mineralogy and Chemistry of Sulfides from the Longqi and Duanqiao Hydrothermal Fields in the Southwest Indian Ridge

Recent investigations found that hydrothermal activity and sulfide mineralization occurs along the Southwest Indian Ridge (SWIR). The Longqi and Duanqiao hydrothermal fields between 49° E and 53° E of the SWIR are two prospective mineralization areas discovered by Chinese scientists. With the aim to determine the mineralogical and chemical characteristics of sulfide minerals, we have conducted detailed studies for samples from the two areas using an optical microscope, X‐ray diffractometer, scanning electron microscope, and electron microprobe. The mineralization processes in the Longqi area are divided into three main stages: (1) the low‐medium‐temperature stage: colloform pyrite (Py I) + marcasite → euhedral pyrite (Py II), (2) the high‐temperature stage: isocubanite (±exsolved chalcopyrite) + pyrrhotite → coarse‐grained chalcopyrite (Ccp I), and (3) the medium–low‐temperature stage: sphalerite + fine‐grained chalcopyrite inclusions (Ccp II) → aggregates of anhedral pyrite (Py III) ± marcasite → Fe‐oxide (‐hydroxide) + amorphous silica. The mineralization processes in the Duanqiao area are divided into two main stages: (1) the medium–high‐temperature stage: subhedral and euhedral pyrite (Py I′) → coarse‐grained chalcopyrite (Ccp I′) and (2) the medium–low‐temperature stage: sphalerite → fine‐grained chalcopyrite (Ccp II′) + chalcopyrite inclusions (Ccp II′) → silica‐cemented pyrite (Py II′) + marcasite → Fe‐oxide + amorphous silica. We suggest that the fine‐grained chalcopyrite inclusions in sphalerite from Longqi and Duanqiao were formed by co‐precipitation and replacement mechanisms, respectively. Primary sphalerites from both fields are enriched in Fe (avg. 5.84 wt% for the Longqi field vs. avg. 3.69 wt% for the Duanqiao field), Co (avg. 185.56 ppm for the Longqi field vs. 160.53 ppm for the Duanqiao field), and Cd (avg. 1950 ppm for the Longqi field vs. avg. 525.26 ppm for the Duanqiao field). Cu contents in pyrite from the Duanqiao field (Py I′: avg. 849.23 ppm and Py II′: avg. 1191.11 ppm) tend to be higher than those from the Longqi field (Py I: avg. 26.67 ppm, Py II: avg. 445 ppm, and Py III: avg. 179.29 ppm). Chalcopyrite from both fields is enriched in Zn (Ccp I: avg. 3226.67 ppm, Ccp II: avg. 9280 ppm, Ccp I′: avg. 848 ppm, Ccp II′ (inclusions): avg. 1098 ppm, and Ccp II′ (fine‐grained): avg. 1795 ppm). The varying contents of Zn in the different pyrite and chalcopyrite generations may result from the zone refining process. An integrated study of the mineralogy and mineralogical chemistry suggests that the hydrothermal fluids of the Longqi area are likely conditioned with higher temperatures and relatively lower fO2 and fS2 than those of the Duanqiao area, but in contrast to the former, the latter is much affected by the compositions of the surrounding rocks

Self-Assembly of Au–Ag Alloy Hollow Nanochains for Enhanced Plasmon-Driven Surface-Enhanced Raman Scattering

In this paper, Au–Ag alloy hollow nanochains (HNCs) were successfully prepared by a template-free self-assembly method achieved by partial substitution of ligands. The obtained Au–Ag alloy HNCs exhibit stronger enhancement as surface-enhanced Raman scattering (SERS) substrates than Au–Ag alloy hollow nanoparticles (HNPs) and Au nanochains substrates with an intensity ratio of about 1.3:1:1. Finite difference time domain (FDTD) simulations show that the SERS enhancement of Au–Ag alloy HNCs substrates is produced by a synergistic effect between the plasmon hybridization effect associated with the unique alloy hollow structure and the strong “hot spot” in the interstitial regions of the nanochains

Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue

Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic. In this work, we contribute a novel modular dialogue system framework that seamlessly integrates factual information and social content into persuasive dialogue. Our framework is generalizable to any dialogue tasks that have mixed social and task contents. We conducted a study that compared user evaluations of our framework versus a baseline end-to-end generation model. We found our framework was evaluated more favorably in all dimensions including competence and friendliness, compared to the end-to-end model which does not explicitly handle social content or factual questions.Comment: To appear in Proceedings of AACL-IJCNLP 2022; 16 pages, 4 figures, 7 table

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19