A statistical characterization of shadowed device-to-device communications in an indoor environment

This paper presents the results of a measurement campaign aimed at characterizing and modeling the indoor radio channel between two hypothetical cellular handsets. The device-to-device channel measurements were made at 868 MHz and investigated a number of different everyday scenarios such as the devices being held at the user's heads, placed in a pocket and one of the devices placed on a desktop. The recently proposed shadowed k-μ fading model was used to characterize these channels and was shown to provide a good description of the measured data. It was also evident from the experiments, that the device-to-device communications channel is susceptible to shadowing caused by the human body.

Indoor off–body communications at 5.8 GHz with multiple antennas at the base station: a statistical characterization using the Nakagami–m fading model

In this paper we investigate the first and second order characteristics of the received signal at the output ofhypothetical selection, equal gain and maximal ratio combiners which utilize spatially separated antennas at the basestation. Considering a range of human body movements, we model the model the small-scale fading characteristics ofthe signal using diversity specific analytical equations which take into account the number of available signal branchesat the receiver. It is shown that these equations provide an excellent fit to the measured channel data. Furthermore, formany hypothetical diversity receiver configurations, the Nakagami-m parameter was found to be close to 1.

“Signal, image and video processing” special issue: Semantic representations for social behavior analysis in video surveillance systems

No abstract available

Propagation modelling and measurements in a populated indoor environment at 5.2 GHz

Human occupants within indoor environments are not always stationary and their movement will lead to temporal channel variations that strongly affect the quality of indoor wireless communication systems. This paper describes a statistical channel characterization, based on experimental measurements, of human body effects on line-of-sight indoor narrowband propagation at 5.2 GHz. The analysis shows that, as the number of pedestrians within the measurement location increases, the Ricean K-factor that best fits the empirical data tends to decrease proportionally, ranging from K=7 with 1 pedestrian to K=0 with 4 pedestrians. Level crossing rate results were Rice distributed, while average fade duration results were significantly higher than theoretically computed Rice and Rayleigh, due to the fades caused by pedestrians. A novel CDF that accurately characterizes the 5.2 GHz channel in the considered indoor environment is proposed. For the first time, the received envelope CDF is explicitly described in terms of a quantitative measurement of pedestrian traffic within the indoor environment.

Molecular surveillance for carbapenemase genes in carbapenem-resistant Pseudomonas aeruginosa in Australian patients with cystic fibrosis.

Summary: The aim of this study was to assess the prevalence of acquired carbapenemase genes amongst carbapenem non-susceptible Pseudomonas aeruginosa isolates in Australian patients with cystic fibrosis (CF). Cross-sectional molecular surveillance for acquired carbapenemase genes was performed on CF P. aeruginosa isolates from two isolate banks comprising: (i) 662 carbapenem resistant P. aeruginosa isolates from 227 patients attending 10 geographically diverse Australian CF centres (2007-2009), and (ii) 519 P. aeruginosa isolates from a cohort of 173 adult patients attending one Queensland CF clinic in 2011. All 1189 P. aeruginosa isolates were tested by polymerase chain reaction (PCR) protocols targeting ten common carbapenemase genes, as well the Class 1 integron intI1 gene and the aadB aminoglycoside resistance gene. No carbapenemase genes were identified among all isolates tested. The intI1 and aadB genes were frequently detected and were significantly associated with the AUST-02 strain (OR 24.6, 95% CI 9.3-65.6; p < 0.0001) predominantly from Queensland patients. Despite the high prevalence of carbapenem resistance in P. aeruginosa in Australian patients with CF, no acquired carbapenemase genes were detected in the study, suggesting chromosomal mutations remain the key resistance mechanism in CF isolates. Systematic surveillance for carbapenemase-producing P. aeruginosa in CF by molecular surveillance is ongoing.

ZIL: An Energy-Efficient Indoor Localization System Using ZigBee Radio to Detect WiFi Fingerprints

In existing WiFi-based localization methods, smart mobile devices consume quite a lot of power as WiFi interfaces need to be used for frequent AP scanning during the localization process. In this work, we design an energy-efficient indoor localization system called ZigBee assisted indoor localization (ZIL) based on WiFi fingerprints via ZigBee interference signatures. ZIL uses ZigBee interfaces to collect mixed WiFi signals, which include non-periodic WiFi data and periodic beacon signals. However, WiFi APs cannot be identified from these WiFi signals by ZigBee interfaces directly. To address this issue, we propose a method for detecting WiFi APs to form WiFi fingerprints from the signals collected by ZigBee interfaces. We propose a novel fingerprint matching algorithm to align a pair of fingerprints effectively. To improve the localization accuracy, we design the K-nearest neighbor (KNN) method with three different weighted distances and find that the KNN algorithm with the Manhattan distance performs best. Experiments show that ZIL can achieve the localization accuracy of 87%, which is competitive compared to state-of-the-art WiFi fingerprint-based approaches, and save energy by 68% on average compared to the approach based on WiFi interface.

Evidence reasoning for event inference in smart transport video surveillance

In this paper we present a new event recognition framework, based on the Dempster-Shafer theory of evidence, which combines the evidence from multiple atomic events detected by low-level computer vision analytics. The proposed framework employs evidential network modelling of composite events. This approach can effectively handle the uncertainty of the detected events, whilst inferring high-level events that have semantic meaning with high degrees of belief. Our scheme has been comprehensively evaluated against various scenarios that simulate passenger behaviour on public transport platforms such as buses and trains. The average accuracy rate of our method is 81% in comparison to 76% by a standard rule-based method.

A Game-Theoretic Approach for Threats Detection and Intervention in Surveillance

Threat prevention with limited security resources is a challenging problem. An optimal strategy is to eectively predict attackers' targets (or goals) based on current available information, and use such predictions to prevent (or disrupt) their planned attacks. In this paper, we propose a game-theoretic framework to address this challenge which encompasses the following three elements. First, we design a method to analyze an attacker's types in order to determine the most plausible type of an attacker. Second, we propose an approach to predict possible targets of an attack and the course of actions that the attackers may take even when the attackers' types are ambiguous. Third, a game-theoretic based strategy is developed to determine the best protection actions for defenders (security resources).

Game-­theoretic Resource Allocation with Real-­time Probabilistic Surveillance Information

Game-theoretic security resource allocation problems have generated significant interest in the area of designing and developing security systems. These approaches traditionally utilize the Stackelberg game model for security resource scheduling in order to improve the protection of critical assets. The basic assumption in Stackelberg games is that a defender will act first, then an attacker will choose their best response after observing the defender’s strategy commitment (e.g., protecting a specific asset). Thus, it requires an attacker’s full or partial observation of a defender’s strategy. This assumption is unrealistic in real-time threat recognition and prevention. In this paper, we propose a new solution concept (i.e., a method to predict how a game will be played) for deriving the defender’s optimal strategy based on the principle of acceptable costs of minimax regret. Moreover, we demonstrate the advantages of this solution concept by analyzing its properties.

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).

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

Gleason Upgrading with Time in a Large Prostate Cancer Active Surveillance Cohort

PURPOSE: We report the percentage of patients on active surveillance who had disease pathologically upgraded and factors that predict for upgrading on surveillance biopsies.

MATERIALS AND METHODS: Patients in our active surveillance database with at least 1 repeat prostate biopsy were included. Histological upgrading was defined as any increase in primary or secondary Gleason grade on repeat biopsy. Multivariate analysis was used to determine baseline and dynamic factors associated with Gleason upgrading. This information was used to develop a nomogram to predict for upgrading or treatment in patients electing for active surveillance.

RESULTS: Of 862 patients in our cohort 592 had 2 or more biopsies. Median followup was 6.4 years. Of the patients 20% were intermediate risk, 0.3% were high risk and all others were low risk. During active surveillance 31.3% of cases were upgraded. On multivariate analysis clinical stage T2, higher prostate specific antigen and higher percentage of cores involved with disease at the time of diagnosis predicted for upgrading. A total of 27 cases (15% of those upgraded) were Gleason 8 or higher at upgrading, and 62% of all 114 upgraded cases went on to have active treatment. The nomogram incorporated clinical stage, age, prostate specific antigen, core positivity and Gleason score. The concordance index was 0.61.

CONCLUSIONS: In this large re-biopsy cohort with medium-term followup, most cases have not been pathologically upgraded to date. A model predicting for upgrading or radical treatment was developed which could be useful in counseling patients considering active surveillance for prostate cancer.