scholarly journals Using residual heat maps to visualise Benford's multi-digit law

2021 ◽  
Author(s):  
Benjamin Hull ◽  
Alexander Cameron Long ◽  
Ifan Glyndwr Hughes
Keyword(s):  
Heat Maps ◽  
Residual Heat

Public Awareness of Preventive Measures against COVID-19: A Google Trends Analysis (Preprint)

2020 ◽  
Author(s):  
Alex Mok ◽  
Oliver Oi Yat Mui ◽  
Kwan Pui Tang ◽  
Chi-Fai NG ◽  
Sunny Hei Wong ◽  
...  
Keyword(s):  
Public Health ◽  
Temporal Trend ◽  
Preventive Measures ◽  
Public Awareness ◽  
Google Trends ◽  
Online Searching ◽  
Effective Measure ◽  
Heat Maps ◽  
Hand Sanitizer ◽  
Incidence Data

BACKGROUND The 2019 coronavirus pandemic (COVID-19) has led to increase in global awareness of related public health preventive measures. The public awareness can be reflected by online searching trends of major search engines, namely Google Trends. OBJECTIVE This study aims to interpret online searches of COVID-19 related public health preventive measures and to identify possible correlations between early search trends and progression of the pandemic. METHODS Search data from five queries “Mask”, “Hand Washing”, “Social Distancing”, “Hand Sanitizer”, and “Disinfectant” were extracted from Google Trends (GT) in the form of Relative Search Volumes (RSV). Global incidence data of COVID-19 was obtained from January 1st to June 30th 2020. Subsequently, the data were analyzed and illustrated in forms of a global temporal RSV trend diagram, a geographical RSV distribution chart, scatter graphs comparing regional RSV with average daily cases; and heat-maps comparing temporal trend of RSV with average daily cases. RESULTS Global temporal trend revealed multiple surges in RSV, which were temporally associated with certain COVID news events. Geographical distribution showed differences of query interests among regions. Although scatter graphs failed to illustrate strong correlations between regional RSV and average daily cases, the heat-maps were able to demonstrate patterns of early RSV peaks in countries with lower average daily cases, for queries “Mask”, “Hand Sanitizer”, and “Disinfectant”, upon incorporating with the temporal element into analysis. CONCLUSIONS Early public awareness of multiple preventive measures was observed in countries with lower daily average cases. Public health authorities may look into early public awareness as an effective measure for future disease control.

Approximate Cluster Heat Maps of Large High-Dimensional Data

2018 ◽  
Author(s):  
Punit Rathore ◽  
James C. Bezdek ◽  
Dheeraj Kumar ◽  
Sutharshan Rajasegarar ◽  
Marimuthu Palaniswami
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Heat Maps

scholarly journals Thin-Film Processing of Polypropylene and Polystyrene Sheets by a Continuous Wave CO2 Laser with the Cu Cooling Base

Polymers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1448
Author(s):  
Nobukazu Kameyama ◽  
Hiroki Yoshida ◽  
Hitoshi Fukagawa ◽  
Kotaro Yamada ◽  
Mitsutaka Fukuda
Keyword(s):  
Thin Film ◽  
Co2 Laser ◽  
Continuous Wave ◽  
Cw Laser ◽  
Heat Effects ◽  
Film Processing ◽  
Carbon Dioxide Co2 ◽  
A Minor ◽  
Residual Heat ◽  
Thin Film Processing

Carbon dioxide (CO2) laser is widely used in commercial and industrial fields to process various materials including polymers, most of which have high absorptivity in infrared spectrum. Thin-film processing by the continuous wave (CW) laser is difficult since polymers are deformed and damaged by the residual heat. We developed the new method to make polypropylene (PP) and polystyrene (PS) sheets thin. The sheets are pressed to a Cu base by extracting air between the sheets and the base during laser processing. It realizes to cut the sheets to around 50 µm thick with less heat effects on the backside which are inevitable for thermal processing using the CW laser. It is considered that the boundary between the sheets and the base is in thermal equilibrium and the base prevents the sheets from deforming to support the backside. The method is applicable to practical use since it does not need any complex controls and is easy to install to an existing equipment with a minor change of the stage.

Capacity Heat Maps for Millimeter Waves

2020 ◽  
Author(s):  
Gabriela Sobreira de Carvalho ◽  
Marcelo Sampaio de Alencar ◽  
Raissa Bezerra Rocha
Keyword(s):  
Millimeter Waves ◽  
Heat Maps

scholarly journals Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Learning Model ◽  
Signal Interference ◽  
Positioning System ◽  
Heat Maps ◽  
Positioning Systems ◽  
Localization Performance ◽  
Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

Sign in / Sign up

Export Citation Format

Share Document