Adapting to time: duration channels do not mediate human time perception

Accurately encoding the duration and temporal order of events is essential for survival and important to everyday activities, from holding conversations to driving in fast flowing traffic. Although there is a growing body of evidence that the timing of brief events (< 1s) is encoded by modality-specific mechanisms, it is not clear how such mechanisms register event duration. One approach gaining traction is a channel-based model; this envisages narrowly-tuned, overlapping timing mechanisms that respond preferentially to different durations. The channel-based model predicts that adapting to a given event duration will result in overestimating and underestimating the duration of longer and shorter events, respectively. We tested the model by having observers judge the duration of a brief (600ms) visual test stimulus following adaptation to longer (860ms) and shorter (340ms) stimulus durations. The channel-based model predicts perceived duration compression of the test stimulus in the former condition and perceived duration expansion in the latter condition. Duration compression occurred in both conditions, suggesting that the channel-based model does not adequately account for perceived duration of visual events.

Key Generation from Wireless Channels: A Review

Key generation from the randomness of wireless channels is a promising alternative to public key cryptography for the establishment of cryptographic keys between any two users. This paper reviews the current techniques for wireless key generation. The principles, performance metrics and key generation procedure are comprehensively surveyed. Methods for optimizing the performance of key generation are also discussed. Key generation applications in various environments are then introduced along with the challenges of applying the approach in each scenario. The paper concludes with some suggestions for future studies.

Shadowed Fading in Indoor Off-Body Communications Channels: A Statistical Characterization using the κ – μ / Gamma Composite Fading Model

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz. This is realized with the aid of the $\kappa-\mu$ / gamma composite fading model which assumes that the transmitted signal undergoes $\kappa-\mu$ fading which is subject to \emph{multiplicative} shadowing. Based on this, the total power of the multipath components, including both the dominant and scattered components, is subject to non-negligible variations that follow the gamma distribution. For this model, we present an integral form of the probability density function (PDF) as well as important analytic expressions for the PDF, cumulative distribution function, moments and moment generating function. In the case of indoor off-body communications, the corresponding measurements were carried out in the context of four explicit individual scenarios namely: line of sight (LOS) and non-LOS (NLOS) walking, rotational and random movements. The measurements were repeated within three different indoor environments and considered three different hypothetical body worn node locations. With the aid of these results, the parameters for the $\kappa-\mu$ / gamma composite fading model were estimated and analyzed extensively. Interestingly, for the majority of the indoor environments and movement scenarios, the parameter estimates suggested that dominant signal components existed even when the direct signal path was obscured by the test subject's body. Additionally, it is shown that the $\kappa-\mu$ / gamma composite fading model provides an adequate fit to the fading effects involved in off-body communications channels. Using the Kullback-Leibler divergence, we have also compared our results with another recently proposed shadowed fading model, namely the $\kappa-\mu$ / lognormal LOS shadowed fading model. It was found that the $\kappa-\mu$ / gamma composite fading model provided a better fit for the majority of the scenarios considered in this study.

Switched Diversity Techniques for Indoor Off-body Communications Channels: An Experimental Analysis and Modeling

This paper investigates the potential improvement in signal reliability for indoor off-body communications channels operating at 5.8 GHz using switched diversity techniques. In particular we investigate the performance of switch-and-stay combining (SSC), switch-and-examine combining (SEC) and switch-and-examine combining with post-examining selection (SECps) schemes which utilize multiple spatially separated antennas at the base station. During the measurements a test subject, wearing an antenna on his chest, performed a number of walking movements towards and then away from a uniform linear array. It was found that all of the considered diversity schemes provided a worthwhile signal improvement. However, the performance of the diversity systems varied according to the switching threshold that was adopted. To model the fading envelope observed at the output of each of the combiners, we have applied diversity specific equations developed under the assumption of Nakagami-$m$ fading. As a measure of the goodness-of-fit, the Kullback-Leibler divergence between the empirical and theoretical probability density functions (PDFs) was calculated and found to be close to 0. To assist with the interpretation of the goodness-of-fit achieved in this study, the standard deviation, $\sigma$, of a zero-mean, $\sigma^2$ variance Gaussian PDF used to approximate a zero-mean, unit variance Gaussian PDF is also presented. These were generally quite close to 1 indicating that the theoretical models provided an adequate fit to the measured data.

Threshold Optimization for Energy Detection-Based Spectrum Sensing Over Hyper-Rayleigh Fading Channels

Spectrum sensing is a key function of cognitive radio systems. Sensing performance is determined by three main factors including the wireless channel between the primary system and the cognitive radio nodes, the detection threshold, and the sensing time. In this letter a closed-form expression for the average probability of detection for energy detection based spectrum sensing over two-wave with diffuse power fading channels is derived. This expression is then used to optimize the detection threshold for cognitive radio nodes, which operate in confined structures that exhibit worse than Rayleigh fading conditions. Such fading conditions can represent a behavioral model of cognitive machine-to-machine systems deployed in enclosed structures such as in-vehicular environments.

Energy Detection based Spectrum Sensing over Two-wave with Diffuse Power Fading Channels

One of the most important factors that affects the performance of energy detection (ED) is the fading channel between the wireless nodes. This article investigates the performance of ED-based spectrum sensing, for cognitive radio (CR), over two-wave with diffuse power (TWDP) fading channels. The TWDP fading model characterizes a variety of fading channels, including well-known canonical fading distributions, such as Rayleigh and Rician, as well as worse than Rayleigh fading conditions modeled by the two-ray fading model. Novel analytic expressions for the average probability of detection over TWDP fading that account for single-user and cooperative spectrum sensing as well as square law selection diversity reception are derived. These expressions are used to analyze the behavior of ED-based spectrum sensing over moderate, severe and extreme fading conditions, and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. Our results indicate that TWDP fading conditions can significantly degrade the sensing performance; however, it is shown that detection performance can be improved when cooperation and diversity are employed. The presented outcomes enable us to identify the limits of ED-based spectrum sensing and quantify the trade-offs between detection performance and energy efficiency for cognitive radio systems deployed within confined environments such as in-vehicular wireless networks.