A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI

Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks

Abstraction and cartographic generalization of geographic user-generated content: use-case motivated investigations for mobile users

On a daily basis, a conventional internet user queries different internet services (available on different platforms) to gather information and make decisions. In most cases, knowingly or not, this user consumes data that has been generated by other internet users about his/her topic of interest (e.g. an ideal holiday destination with a family traveling by a van for 10 days). Commercial service providers, such as search engines, travel booking websites, video-on-demand providers, food takeaway mobile apps and the like, have found it useful to rely on the data provided by other users who have commonalities with the querying user. Examples of commonalities are demography, location, interests, internet address, etc. This process has been in practice for more than a decade and helps the service providers to tailor their results based on the collective experience of the contributors. There has been also interest in the different research communities (including GIScience) to analyze and understand the data generated by internet users. The research focus of this thesis is on finding answers for real-world problems in which a user interacts with geographic information. The interactions can be in the form of exploration, querying, zooming and panning, to name but a few. We have aimed our research at investigating the potential of using geographic user-generated content to provide new ways of preparing and visualizing these data. Based on different scenarios that fulfill user needs, we have investigated the potential of finding new visual methods relevant to each scenario. The methods proposed are mainly based on pre-processing and analyzing data that has been offered by data providers (both commercial and non-profit organizations). But in all cases, the contribution of the data was done by ordinary internet users in an active way (compared to passive data collections done by sensors). The main contributions of this thesis are the proposals for new ways of abstracting geographic information based on user-generated content contributions. Addressing different use-case scenarios and based on different input parameters, data granularities and evidently geographic scales, we have provided proposals for contemporary users (with a focus on the users of location-based services, or LBS). The findings are based on different methods such as semantic analysis, density analysis and data enrichment. In the case of realization of the findings of this dissertation, LBS users will benefit from the findings by being able to explore large amounts of geographic information in more abstract and aggregated ways and get their results based on the contributions of other users. The research outcomes can be classified in the intersection between cartography, LBS and GIScience. Based on our first use case we have proposed the inclusion of an extended semantic measure directly in the classic map generalization process. In our second use case we have focused on simplifying geographic data depiction by reducing the amount of information using a density-triggered method. And finally, the third use case was focused on summarizing and visually representing relatively large amounts of information by depicting geographic objects matched to the salient topics emerged from the data

Bending Response of Timber Mortise and Tenon Joints Reinforced with Filler-Modules and FRP Gussets

In 2013, the California Bay Area (CBA) passed a set of ordinances to ensure that their 10,000 plus timber soft-story buildings were prepared for seismic events, through nondestructive evaluation methods. Many property owners are searching for an affordable retrofitting system that will also meet CBA’s new laws focusing on installations by the mandated deadlines in 2020. Over the past three to four decades, Fiber Reinforced Polymer (FRP) composites have found their way into the civil infrastructure sector for rehabilitation. The objective of this study is to evaluate the bending behavior retrofitted mortise and tenon timber joints reinforced with engineered wood filler-modules and FRP composite gussets. In addition failure modes with filler-modules and/or gussets are evaluated for future studies to develop design methodologies. The above efforts are focused on a West Virginia University (WVU) patented joint performance enhancement system. A total of ten specimens with varying retrofitting schemes were studied by using a static bending test. A 6” by 6” timber post joining system with a mortise and tenon connection style were used throughout this study. These bending tests are conducted and reported herein to determine the predominate failure modes based off of the inclusion of filler-modules and gussets. The increase in load capacity and energy absorption after retrofitting the joints subjected to bending and shear, and also the limit states on deflections and deformations of timber joints after incorporating the proposed retrofit schemes, are evaluated from the test data and reported herein. The results from the tests indicate that a joint with filler-modules and a FRP gusset with an enhanced stiffness will perform better than a conventional joint system. The control specimens, i.e. without retrofit schemes, averaged a maximum load of ~2,900 lbs. A retrofitted specimen was able to reach a maximum load of ~16,000 lbs. Conventional beam-column mortise and tenon joints made of 6” x 6” size could only deflect 0.29” at the peak load with a 18” bending span, while a retrofitted joint with filler-modules and gussets was able to reach 0.96” at peak load. Joints retrofit with the proposed scheme also show up to 500% increase in energy absorption. Mortise and tenon joints have a shear failure on tenon when the dowel is made of a harder wood than the beam and column but will have a dowel bending/shear failure if it is softer than the bearing material. Joints retrofitted with filler-modules were able to prevent the tenon failure but failed once the filler-module on the tension side would debond. To delay the debond, FRP gussets were installed which resulted in FRP rupture failure at the tip of the joint area or beam-column interface. The retrofit system proposed in this paper displayed an increase in strength, ductility, and energy absorption by factors of about 3 to 5. The data herein proves that the filler-module and gusset combination is an effective retrofitting scheme. The system is an easy to learn installation process which can be implemented cost-effectively in a short amount of time and would save millions, in both the rehabilitation and retrofit costs

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

The impact of 3D virtual environments with different levels of realism on route learning: a focus on age-based differences

With technological advancements, it has become notably easier to create virtual environments (VEs) depicting the real world with high fidelity and realism. These VEs offer some attractive use cases for navigation studies looking into spatial cognition. However, such photorealistic VEs, while attractive, may complicate the route learning process as they may overwhelm users with the amount of information they contain. Understanding how much and what kind of photorealistic information is relevant to people at which point on their route and while they are learning a route can help define how to design virtual environments that better support spatial learning. Among the users who may be overwhelmed by too much information, older adults represent a special interest group for two key reasons: 1) The number of people over 65 years old is expected to increase to 1.5 billion by 2050 (World Health Organization, 2011); 2) cognitive abilities decline as people age (Park et al., 2002). The ability to independently navigate in the real world is an important aspect of human well-being. This fact has many socio-economic implications, yet age-related cognitive decline creates difficulties for older people in learning their routes in unfamiliar environments, limiting their independence. This thesis takes a user-centered approach to the design of visualizations for assisting all people, and specifically older adults, in learning routes while navigating in a VE. Specifically, the objectives of this thesis are threefold, addressing the basic dimensions of: ❖ Visualization type as expressed by different levels of realism: Evaluate how much and what kind of photorealistic information should be depicted and where it should be represented within a VE in a navigational context. It proposes visualization design guidelines for the design of VEs that assist users in effectively encoding visuospatial information. ❖ Use context as expressed by route recall in short- and long-term: Identify the implications that different information types (visual, spatial, and visuospatial) have over short- and long-term route recall with the use of 3D VE designs varying in levels of realism. ❖ User characteristics as expressed by group differences related to aging, spatial abilities, and memory capacity: Better understand how visuospatial information is encoded and decoded by people in different age groups, and of different spatial and memory abilities, particularly while learning a route in 3D VE designs varying in levels of realism. In this project, the methodology used for investigating the topics outlined above was a set of controlled lab experiments nested within one. Within this experiment, participants’ recall accuracy for various visual, spatial, and visuospatial elements on the route was evaluated using three visualization types that varied in their amount of photorealism. These included an Abstract, a Realistic, and a Mixed VE (see Figure 2), for a number of route recall tasks relevant to navigation. The Mixed VE is termed “mixed” because it includes elements from both the Abstract and the Realistic VEs, balancing the amount of realism in a deliberate manner (elaborated in Section 3.5.2). This feature is developed within this thesis. The tested recall tasks were differentiated based on the type of information being assessed: visual, spatial, and visuospatial (elaborated in Section 3.6.1). These tasks were performed by the participants both immediately after experiencing a drive-through of a route in the three VEs and a week after that; thus, addressing short- and long-term memory, respectively. Participants were counterbalanced for their age, gender, and expertise while their spatial abilities and visuospatial memory capacity were controlled with standardized psychological tests. The results of the experiments highlight the importance of all three investigated dimensions for successful route learning with VEs. More specifically, statistically significant differences in participants’ recall accuracy were observed for: 1) the visualization type, highlighting the value of balancing the amount of photorealistic information presented in VEs while also demonstrating the positive and negative effects of abstraction and realism in VEs on route learning; 2) the recall type, highlighting nuances and peculiarities across the recall of visual, spatial, and visuospatial information in the short- and long-term; and, 3) the user characteristics, as expressed by age differences, but also by spatial abilities and visuospatial memory capacity, highlighting the importance of considering the user type, i.e., for whom the visualization is customized. The original and unique results identified from this work advance the knowledge in GIScience, particularly in geovisualization, from the perspective of the “cognitive design” of visualizations in two distinct ways: (i) understanding the effects that visual realism has—as presented in VEs—on route learning, specifically for people of different age groups and with different spatial abilities and memory capacity, and (ii) proposing empirically validated visualization design guidelines for the use of photorealism in VEs for efficient recall of visuospatial information during route learning, not only for shortterm but also for long-term recall in younger and older adults