A Dual-Channel System for Providing Location Estimation in Mobile Computing

2003 ◽  
Vol 04 (03) ◽  
pp. 271-290 ◽  
Author(s):  
Stephen Ka Chun Chan ◽  
Kenny Ka Ho Kan ◽  
Joseph Kee-Yin Ng
Keyword(s):  
Urban Areas ◽  
Blind Spot ◽  
Location Estimation ◽  
Signal Attenuation ◽  
Channel System ◽  
Location Services ◽  
Estimation Algorithms ◽  
Mobile Operator ◽  
Dual Channel ◽  
Gsm Network

A dual channel system, which is based on the GPS and the GSM Network, is being developed to compensate the problem of the lost of GPS signals in providing location services to mobile users in urban areas. In this design, when GPS signals are being blocked in blind spot areas, GSM positioning algorithms would be used as an alternative method to provide location estimations. This research is an investigation in search of a set of location estimation algorithms based on signal attenuation to work with GPS, so as to develop a dual channel positioning system. With the technical support from a local mobile operator, we have constructed and conducted several real world experiments for our investigation and results are promising.

Manufacturers' and distributors' capabilities influencing dual channel choice

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ryuta Ishii
Keyword(s):  
System Performance ◽  
Multinomial Logistic Regression ◽  
Product Line ◽  
Channel Structure ◽  
Channel System ◽  
Content Type ◽  
Multinomial Logistic Regression Analysis ◽  
Dual Channel ◽  
Channel Strategy ◽  
Dual Channels

PurposeMany manufacturers implement a dual channel strategy, which can be defined as the simultaneous use of integrated and independent channels of distribution for the same product line. This study employs the resource-based theory and examines how manufacturers' and distributors' capabilities affect manufacturers' choices of dual channel strategy. The study also examines how fit between organisational capability and channel structure affects channel system performance.Design/methodology/approachEmpirical testing was conducted using survey data collected from 262 Japanese business-to-business manufacturers. This study performed a multinomial logistic regression analysis to examine the antecedents of dual channel strategy and a t-test to examine its performance implications.FindingsThe results show that manufacturers' information capabilities and the availability of distributors' selling capabilities affect whether manufacturers choose a dual channel strategy, and that market turbulence moderates the effects of these two capability factors. The results also indicate that manufacturers can improve their channel system performance by choosing channel strategies that fit organisational capabilities.Originality/valueMost previous studies employ transaction cost theory to identify the factors driving the choice of dual channel strategy. However, these studies neglect the heterogeneity of resources/capabilities. Little is known about whether capability factors affect the dual channel strategy, and how this choice can be linked to channel system performance. By addressing this knowledge gap, this study contributes to enhance our understanding of dual channels.

scholarly journals Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

2021 ◽  
Vol 13 (18) ◽  
pp. 3574
Author(s):  
Jamon Van Den Hoek ◽  
Hannah K. Friedrich
Keyword(s):  
Urban Areas ◽  
Spatial Epidemiology ◽  
Demographic Data ◽  
Satellite Image ◽  
Blind Spot ◽  
Probability Of Detection ◽  
Small Scale ◽  
Human Settlement ◽  
Demographic Modeling ◽  
Footprint Area

Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product’s areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches.

scholarly journals Our Collective Needs and Strengths: Urban AI/ANs and the COVID-19 Pandemic

2021 ◽  
Vol 6 ◽  
Author(s):  
Tara L. Maudrie ◽  
Kerry Hawk Lessard ◽  
Jessica Dickerson ◽  
Kevalin M. W. Aulandez ◽  
Allison Barlow ◽  
...  
Keyword(s):  
Alaska Native ◽  
Urban Areas ◽  
Blind Spot ◽  
Health Programs ◽  
Local Data ◽  
Baltimore City ◽  
Care Community ◽  
Community Needs ◽  
Urban Indian ◽  
Health Assessments

The COVID-19 pandemic has raised national consciousness about health inequities that disproportionately impact American Indian/Alaska Native (AI/AN) communities, yet urban AI/AN communities continue to remain a blind spot for health leaders and policymakers. While all United States cities have been the traditional homelands of AI/AN peoples since time immemorial, urban AI/ANs are consistently excluded in local and national health assessments, including recent reports pertaining to COVID-19. Today the majority of AI/ANs (71%) live in urban areas, and many cities have strong Urban Indian Health Programs (UIHPs) that provide space for medical care, community gatherings, cultural activities, and traditional healing. Many of these UIHPs are currently scrambling to meet the needs of their AI/AN service communities during the pandemic. While the COVID-19 pandemic brought new sources of funding to UIHPs, the lack of local AI/AN data and arbitrary funding restrictions precluded some UIHPs from addressing their communities’ most immediate challenges such as food and economic insecurities. Despite these challenges, urban AI/AN communities carry the historical resilience of their ancestors as they weave strong community networks, establish contemporary traditions, and innovate to meet community needs. This article focuses on the experiences of one UIHP in Baltimore City during the COVID-19 pandemic to illustrate present-day challenges and strengths, as well as illuminate the urgency for tailored, local data-driven public health approaches to urban AI/AN health.

scholarly journals Retrieval of rainfall fields in urban areas using attenuation measurements from mobile phone networks: a modeling feasibility study

2016 ◽  
Author(s):  
Bahtiyor Zohidov ◽  
Hervé Andrieu ◽  
Myriam Servières ◽  
Nicolas Normand
Keyword(s):  
Mobile Phone ◽  
Urban Areas ◽  
Rainfall Variability ◽  
A Priori ◽  
Rain Attenuation ◽  
Retrieval Algorithm ◽  
A Priori Knowledge ◽  
Signal Attenuation ◽  
Retrieval Performance ◽  
Priori Knowledge

Abstract. Rainfall monitoring is an important global issue in urban hydrological applications, such as flood warning and water resources management systems. Until the present time, rain gauges and weather radars have been widely used as sensors to provide rainfall information with a detailed resolution; most cities in the world however are inadequately equipped. Recently, commercial microwave links (MWL) have been proposed as a new means of monitoring space-time rainfall. A transmitted signal along such links is known to be attenuated by rainfall, hence the measurement of this signal attenuation could serve to estimate path-averaged rainfall intensity. The density of commercial MWL is typically high in most cities today, which raises new questions over the possibility of retrieving rainfall using signal attenuation data from multiple links. The objective of this article is to assess the feasibility of retrieving rainfall fields in urban areas using rain attenuation data from commercial MWL that are mainly operated by mobile phone companies. This work is based on a simulation framework applied to a real case study. The study area is the city of Nantes, France. Rainfall datasets containing 207 weather radar images recorded by the Météo-France Agency's C-band at high spatial (250 m × 250 m) and temporal (5 min) resolutions are first used to generate rain attenuation data over the existing mobile phone network, which combines 256 microwave links operating at 18, 23 and 38 GHz. The rain attenuation data generated are used as a real signal dataset. A novel retrieval algorithm is then proposed to convert the rain-induced attenuation data into a rainfall map. A priori knowledge introduced to initialize the algorithm heavily influences retrieval performance if the problem to be solved is under-determined, as is the case herein. The capabilities as well as limitations of the retrieval algorithm, as regards capturing different rainfall variability, are evaluated. A detailed sensitivity analysis, carried out with respect to various parameters including a priori knowledge, decorrelation distance, and the retrieval performance of the algorithm depending on the density level of the MWL network is also evaluated in a light rain, a shower and amidst storm events. The conclusion, based on 200+ retrieval tests, states that the proposed algorithm is capable of capturing high rainfall variability in the presence of large measurement error sources according to the adopted methodology.

scholarly journals An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

2020 ◽  
Vol 9 (4) ◽  
pp. 261
Author(s):  
Fan Xu ◽  
Xuke Hu ◽  
Shuaiwei Luo ◽  
Jianga Shang
Keyword(s):  
Indoor Localization ◽  
Attenuation Factor ◽  
Access Point ◽  
Location Estimation ◽  
Signal Attenuation ◽  
Distance Ratio ◽  
Localization Accuracy ◽  
Access Points ◽  
Attenuation Parameter ◽  
The Impact

Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

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