Indoor and Outdoor monitoring of photocatalytic activity using a mobile phone app. and a photocatalytic activity indicator ink (paii)

A resazurin (Rz) based photocatalyst activity indicator ink (paii) is used to test the activity of commercial self-cleaning materials. The semiconductor photocatalyst driven colour change of the ink is monitored indoors and outside using a simple mobile phone application that measures the RGB colour components of the digital image of the paii-covered, irradiated sample in real time. The results correlate directly with those generated using a traditional, lab-bound method of analysis (UV–vis spectrophotometry).

A survey of location inference techniques on Twitter

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author’s location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.

Received Signal Characteristics of the Indoor Off-Body Communications Channel at 5.8 GHz

In this paper we investigate the received signal characteristics of a mobile chest-worn transmitter at 5.8 GHz within a high multipath indoor environment. The off-body channel measurements considered both the co- and cross-polarized received signal for both line-of-sight (LOS) and non-LOS (NLOS) conditions. A straightforward channel model based upon the estimated path loss, a lognormal slow fading component and Ricean small-scale fading contribution is developed and used to perform simulations which allow the generation of first order received signal power characteristics.

Channel characterisation for indoor wearable active RFID at 868 MHz

Active radio-frequency identification systems that are used for the localisation and tracking of people will be subject to the same body centric processes that impact other forms of wearable communications. To achieve the goal of creating body worn tags with multiyear life spans, it will be necessary to gain an understanding of the channel conditions which are likely to impact the reader-tag interrogation process. In this paper we present the preliminary results of an indoor channel measurement campaign conducted at 868 MHz aimed at understanding and modelling signal characteristics for a wrist-worn tag. Using a model selection process based on the Akaike Information Criterion, the lognormal distribution was selected most often to describe the received signal amplitude. Parameter estimates are provided so that the channels investigated in this study may be readily simulated.

Localization Algorithm Performance in Ultra Low Power Active RFID Based Patient Tracking

Indoor personnel localization research has generated a range of potential techniques and algorithms. However, these typically do not account for the influence of the user's body upon the radio channel. In this paper an active RFID based patient tracking system is demonstrated and three localization algorithms are used to estimate the location of a user within a modern office building. It is shown that disregarding body effects reduces the accuracy of the algorithms' location estimates and that body shadowing effects create a systematic position error that estimates the user's location as closer to the RFID reader that the active tag has line of sight to.

Supervisor Continuity or Co-Location: Which Matters in Residency Education? Findings From a Qualitative Study of Remote Supervisor Family Physicians in Australia and Canada

Purpose: Changes to health care systems andworking hours have fragmentedresidents’ clinical experiences withpotentially negative effects ontheir development as professionals.Investigation of off-site supervision,which has been implemented in isolatedrural practice, could reveal importantbut less overt components of residencyeducation. 

Method: Insights from sociocultural learningtheory and work-based learning provideda theoretical framework. In 2011–2012,16 family physicians in Australia andCanada were asked in-depth how theyremotely supervised residents’ workand learning, and for their reflectionson this experience. The verbatiminterview transcripts and researchers’memos formed the data set. Templateanalysis produced a description andinterpretation of remote supervision. 

Results: Thirteen Australian family physiciansfrom five states and one territory, andthree Canadians from one province,participated. The main themes werehow remoteness changed the dynamicsof care and supervision; the importanceof ongoing, holistic, nonhierarchical,supportive supervisory relationships; andthat residents learned “clinical courage”through responsibility for patients’ careover time. Distance required supervisorsto articulate and pass on their expertiseto residents but made monitoringdifficult. Supervisory continuityencouraged residents to build on pastexperiences and confront deficiencies. 

Conclusions: Remote supervision enabled residents todevelop as clinicians and professionals.This questions the supremacy of co-locationas an organizing principle forresidency education. Future specialists maybenefit from programs that give themongoing and increasing responsibilityfor a group of patients and supportive.

A Statistical Characterization of Shadowed Fading in Indoor Off-Body Communications Channels At 5.8 GHz

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz using the κ-μ / gamma composite fading model. Realistic measurements have been conducted considering four individual scenarios namely line of sight (LOS) and non-LOS (NLOS) walking, rotation and random movements within an indoor laboratory environment. It is shown that the κ-μ / gamma composite fading model provides a better fit to the fading observed in off-body communications channels compared to the conventional Nakagami-m and Rician fading models.

Signal Reception Characteristics in the Proximity of Alice and Bob for Secure Indoor Peer-to-Peer Communications At 2.45 GHz

Mutual variation of the received signal which occurs as a consequence of the channel reciprocity property has recently been proposed as a viable method for secret key generation. However, this cannot be strictly maintained in practice as the property is applicable only in the absence of interference. To ensure the propagation defined key remains secret, one requirement is that there remain high degrees of uncertainty between the legitimate users channel response and that of any eavesdropper's. In this paper, we investigate whether such de-correlation occurs for an indoor point-to-point link at 2.45 GHz. This is achieved by computing the localized correlation coefficient between the simultaneous channel response measured by the legitimate users and that of multiple distributed eavesdroppers for static and dynamic scenarios.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.