scholarly journals THE ACCURACY OF ALGORITHMS FOR WLAN INDOOR POSITIONING AND THE STANDARDIZATION OF SIGNAL RECEPTION FOR DIFFERENT MOBILE DEVICES

2014 ◽  
pp. 103-109
Author(s):  
Uwe Grossmann ◽  
Markus Schauch ◽  
Syuzanna Hakobyan
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Indoor Positioning ◽  
Base Station ◽  
Personal Digital Assistants ◽  
Strength Index ◽  
Average Accuracy ◽  
Different Types ◽  
Seminar Room

Often mobile devices like mobile phones or personal digital assistants (PDA) are equipped with IEEE 802.11 WLAN adapters. Furthermore, within many buildings a WLAN infrastructure is available. The aim of this work is to investigate the quality of different indoor positioning methods based on values of WLAN received signal strength index (RSSI) using commercially available devices (mobile phones, PDA). Three positioning algorithms were considered: minimal Euclidian distance, intersections of RSSI-Isolines and a stochastic model based on Bayes' theorem. Two measuring testbeds, a museum's exhibition room and an empty seminar room, four different types of PDA (Dell, Fujitsu, HP, T-Mobile) and two types of access points (Netgear, Lancom) were used. Results show that positioning can be achieved with an average accuracy of approx. 2-3 metres. WLAN capable mobile devices behave differently in receiving RSSI values of a base station. For reasons of standardization a linear correlation between different receiving levels of PDA was investigated.

scholarly journals RSSI-BASED WLAN INDOOR POSITIONING USED WITHIN A DIGITAL MUSEUM GUIDE

2014 ◽  
pp. 66-72
Author(s):  
U. Grossmann ◽  
S. Gansemer ◽  
O. Suttorp
Keyword(s):  
Indoor Positioning ◽  
Personal Digital Assistants ◽  
Test Bed ◽  
Strength Index ◽  
Frequency Pattern ◽  
Digital Museum ◽  
Museum Guide ◽  
Different Types ◽  
Seminar Room

As modern mobile devices are often equipped with a WLAN interface and many buildings are equipped with a WLAN infrastructure, WLAN could be used for localization. The aim of this work is to investigate the quality of different indoor positioning methods based on values of WLAN received signal strength index (RSSI) using commercially available devices (smart phones, personal digital assistants). Three positioning algorithms were considered: minimal Euclidian distance, intersections of RSSI-Isolines and a stochastic model based on Bayes' theorem. A measuring test bed within an empty seminar room, four different types of personal digital assistants (Dell, Fujitsu, HP, T-Mobile) and two types of access points (Netgear, Lancom) were used. No modification of hardware and software was conducted. Furthermore this paper shows how WLAN localization can be applied in a museum guide application. This paper also shows the visualization of spatial user position frequency pattern during a field trial within a museum.

The mHealth Stack

2012 ◽  
pp. 1-17
Author(s):  
Benjamin Falchuk ◽  
David Famolari ◽  
Russell Fischer ◽  
Shoshana Loeb ◽  
Euthimios Panagos
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Healthcare Services ◽  
Cloud Services ◽  
Healthcare Applications ◽  
Patient Centered ◽  
Mobile Healthcare ◽  
Care Givers ◽  
Current Usage

Applications accessible through mobile devices, such as mobile phones, are playing an increasingly important part in the delivery of high quality and personalized healthcare services. In this paper, we examine current usage of mobile devices and networks by mobile healthcare applications, and present our views on how mobile devices and networks could be used for creating patient-centered healthcare applications. The patient-centered healthcare paradigm allows for increased quality of care and quality of life for patients while increasing personal freedom to move about and be always connected to care-givers and healthcare services. The structure of our discussion is analogous to layered protocol stack in communications, progressing from the network and radio technologies, servicing middleware, cloud services, health sensors, mobile smartphones, and applications. All these layers come into play to support future mobile healthcare services.

Application Security for Mobile Devices1

2012 ◽  
pp. 266-284
Author(s):  
Gabriele Costa ◽  
Aliaksandr Lazouski ◽  
Fabio Martinelli ◽  
Paolo Mori
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Scientific Literature ◽  
Personal Data ◽  
Personal Digital Assistants ◽  
Application Security ◽  
Contact List ◽  
E Mail

In these last years, mobile devices, such as mobile phones or Personal Digital Assistants, became very popular among people. Moreover, mobile devices became also very powerful, and most of them are also able to execute applications, such as games, Internet browsers, e-mail clients, and so on. Hence, an adequate security support is required on these devices, to avoid that malicious applications damage the device or perform unauthorized accesses to personal data (such as the contact list). This chapter describes the approaches that have been proposed in scientific literature to guarantee the security of mobile devices.

Mobile Design for Older Adults

2008 ◽  
pp. 624-634 ◽  
Author(s):  
Katie A. Siek
Keyword(s):  
Quality Of Life ◽  
Older People ◽  
Mobile Phones ◽  
Personal Digital Assistants ◽  
Work Related ◽  
Age Related ◽  
Mobile Design ◽  
Information Appliances ◽  
Global Population

The global population of older people is steadily growing and challenging researchers in the human computer interaction community to design technologies to help them remain independent and preserve their quality of life. Researchers are addressing this challenge by creating assistive technology solutions using information appliances, such as personal digital assistants and mobile phones. Some have questioned whether older people can use information appliances because of age related problems. This chapter discusses work related to designing, implementing, and evaluating mobile applications for the aging. A discussion about what researchers should consider during the design process for information appliances shows the unique challenges posed by this population.

Web Content Adaptation Frameworks and Techniques

2011 ◽  
pp. 1070-1077
Author(s):  
Tiong-Thye Goh ◽  
Kinshuk
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Web Application ◽  
Learning Systems ◽  
Personal Digital Assistants ◽  
Content Adaptation ◽  
Web Pages ◽  
Web Content ◽  
Application Access ◽  
The Web

Most Web pages are designed with desktop platform access in mind, but with the proliferation of mobile devices such as Personal Digital Assistants (PDAs) and mobile phones, accessing Web pages through a variety of devices without proper content adaptation can result in an aesthetically unpleasant, un-navigable and, in most cases, unsatisfying experience. This article provides an overview of approaches in Web content adaptation framework and techniques being developed to extend the Web application access to non-desktop platforms. After describing general adaptation techniques, the article focuses particularly on the adaptation requirements of learning systems, especially when they are accessed through mobile devices.

Exploring the Use of Mobile Devices to Support Teacher Education

2015 ◽  
pp. 466-478
Author(s):  
Jocelyn Wishart
Keyword(s):  
Teacher Education ◽  
Student Teachers ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Initial Teacher Education ◽  
Handheld Devices ◽  
Personal Digital Assistants ◽  
Learning And Teaching ◽  
Support Teacher ◽  
The Uk

Early research on personal digital assistants (PDAs), forerunners of today's Smartphones, shows they have the potential to support pre-service teachers' learning and teaching on placement in schools. This article reports results from three such projects conducted with small groups of graduate student teachers in the UK which indicate that handheld devices are particularly supportive of management of learning and teaching and of building knowledge across contexts. However, mobile phones are viewed in schools as disruptive devices and it became apparent that social pressures on trainees using devices that are, in most schools, banned to pupils were impacting negatively on their use of the technology to support their learning. It is therefore argued that, whilst schools are mostly viewed as learning organisations that support staff professional development, in the case of emerging technologies, this isn't always the case and we need to do more to enable the realisation of these benefits of using mobile devices to support initial teacher education.

scholarly journals Can We Rely on Mobile Devices and Other Gadgets to Assess the Postural Balance of Healthy Individuals? A Systematic Review

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2972 ◽  
Author(s):  
Alexandre S. Pinho ◽  
Ana P. Salazar ◽  
Ewald M. Hennig ◽  
Barbara C. Spessato ◽  
Antoinette Domingo ◽  
...  
Keyword(s):  
Systematic Review ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Postural Balance ◽  
Inertial Sensors ◽  
Cost Benefit ◽  
Healthy Adults ◽  
Discriminatory Power ◽  
Healthy Individuals

The consequences of falls, costs, and complexity of conventional evaluation protocols have motivated researchers to develop more effective balance assessments tools. Healthcare practitioners are incorporating the use of mobile phones and other gadgets (smartphones and tablets) to enhance accessibility in balance evaluations with reasonable sensitivity and good cost–benefit. The prospects are evident, as well as the need to identify weakness and highlight the strengths of the different approaches. In order to verify if mobile devices and other gadgets are able to assess balance, four electronic databases were searched from their inception to February 2019. Studies reporting the use of inertial sensors on mobile and other gadgets to assess balance in healthy adults, compared to other evaluation methods were included. The quality of the nine studies selected was assessed and the current protocols often used were summarized. Most studies did not provide enough information about their assessment protocols, limiting the reproducibility and the reliability of the results. Data gathered from the studies did not allow us to conclude if mobile devices and other gadgets have discriminatory power (accuracy) to assess postural balance. Although the approach is promising, the overall quality of the available studies is low to moderate.

scholarly journals Improved Association and Disassociation Scheme for Enhanced WLAN Handover and VHO

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Sungjin Shin ◽  
Donghyuk Han ◽  
Hyoungjun Cho ◽  
Jong-Moon Chung
Keyword(s):  
Mobile Devices ◽  
Local Area Networks ◽  
Access Point ◽  
Local Area ◽  
Vertical Handover ◽  
Base Station ◽  
Wireless Local Area Networks ◽  
Received Signal Strength Indicator ◽  
Wireless Protocol

The number of mobile devices and wireless connections is significantly increasing. Among many wireless protocol types, wireless local area networks (WLANs) are expected to support a significant number of devices. Due to this reason, effective and efficient handover (HO) and vertical handover (VHO) support for WLAN mobile devices is important. A significant improvement in quality of service (QoS) can be obtained by reducing the association and disassociation interruption time for user equipment (UE) servicing real-time applications during WLAN HO and VHO operations. Based on this focus, this paper investigates the problem of using only the received signal strength indicator (RSSI) in HO and VHO decisions, which is what the current IEEE 802.11 based WLAN systems use. Experimental results presented in this paper demonstrate that only using the RSSI results in significant interruption time during HO to another WLAN access point (AP) or to a cellular base station during VHO. Therefore, in this paper, an improved association and disassociation scheme that can reduce the data interruption time (DIT) and improve the throughput performance is proposed.

Databases for Mobile Applications

2009 ◽  
pp. 870-880
Author(s):  
Yip Yee Shing ◽  
Chan Lit Tin ◽  
Shiu Ka Wai ◽  
Indranil Bose ◽  
Wang Ping ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Mobile Applications ◽  
Rapid Development ◽  
Form Factors ◽  
Handheld Computers ◽  
Personal Digital Assistants ◽  
Functional Capabilities ◽  
Notebook Computers ◽  
Hardware Platforms

Owing to the rapid development of mobile technology over the past few decades, there have been many different kinds of mobile devices emerging in the market, and most of them work with databases seamlessly. Mobile phone gaming, downloading of ringtones, and e-calendaring are some of the prominent examples of mobile applications that require the close integration of mobile devices with databases. Mobile devices take various forms and configurations. The packaging, form factors, hardware platforms, operating system support, and functional capabilities vary across these devices. There are, however, many common attributes shared by the devices, such as notebook computers, pen-based computers, handheld computers, and the like, all of which are used in mobile computing. These devices can be categorized into the following categories according to their functionalities and features, as detailed in Dhawan (1997). They are: • notebook computers • personal digital assistants • tablet computers • hybrid mobile devices • mobile phones In this article, we focus on personal digital assistants (PDA) and mobile phones as they are the most popular and commonly used mobile devices in the industry.

scholarly journals Classification of Watermelon using Sound Processing.

2020 ◽  
Vol 9 (4) ◽  
pp. 2489-2492
Keyword(s):  
Naive Bayes ◽  
Random Tree ◽  
Human Beings ◽  
Sound Processing ◽  
Sound File ◽  
Average Accuracy ◽  
Different Types ◽  
Sound Files

In a country like India, wide variety of fruits are available. Fruits plays an important role in the health of human beings and naturally health improves, if the quality of the fruit is good. Grading of the watermelon quality helps the consumers and vendors. The proposed work is to classify the watermelons based on the sound. Sound file dataset is created manually by tapping the watermelon and recording the sound. Dataset consist of different types of watermelon. For this, different size, colour and shape of the watermelons are used. Features are extracted from the sound files. Naïve Bayes, SMO and Random Tree classifiers are used for classification. The proposed work has achieved average accuracy of 78.8 %.

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