People's body movements in daily face-to-face communication influence each other. For instance, during a heated debate, the participants use more gestures and other body movements, while in a calm discussion they use fewer gestures. This “coevolution” of interpersonal body movements occurs on multiple time scales, like minutes or hours. However, the multi-time-scale coevolution in daily communication is not clear yet. In this paper, we explore the minute-to-minute coevolution of interpersonal body movements in daily communication and investigate the characteristics of this coevolution. We present quantitative data on upper-body movements from thousand test subjects from seven organizations gathered over several months via wearable sensors. The device we employed measured upper-body movements with an accelerometer and the duration of face-to-face communication with an infrared ray sensor on a minute-by-minute basis. We defined a coevolution measure between two people as the number of per-minute changes of their body movement and compared the indices for face-to-face and non-face-to-face situations. We found that on average, the amount of people's body movements changed correspondingly for face-to-face communication and that the average rate of coevolution in the case of face-to-face communication was 3-4% higher than in the case of non-face-to-face situation. These results reveal minute-to-minute coevolution of upper-body movements between people in daily communication. The finding suggests that the coevolution of body movement arises in multiple time scales.

We consider a queuing model with applications to electric vehicle (EV) charging systems in smart grids. We adopt a scheme where an Electric Service Company (ESCo) broadcasts a one bit signal to EVs, possibly indicating 'on-peak' periods during which electricity cost is high. EVs randomly suspend/resume charging based on the signal. To model the dynamics of EVs we propose an M/M/∞ queue with random interruptions, and analyze the dynamics using time-scale decomposition. There exists a trade-off: one may postpone charging activity to 'off-peak' periods during which electricity cost is cheaper, however this incurs extra delay in completion of charging. Using our model we characterize achievable trade-offs between the mean cost and delay perceived by users. Next we consider a scenario where EVs respond to the signal based on the individual loads. Simulation results show that peak electricity demand can be reduced if EVs carrying higher loads are less sensitive to the signal.

In this paper, the electric field radiated by a lightning discharge is derived in the time-frequency domain. By modeling a tortuous and branched lighting discharge, we computed the discrete wavelet transform of the radiated electric field, providing time localization of the fine structure of the field, which is though to be related to the discharge path geometry. By solving the radiated field in the wavelet domain, we aim at simulating the effects of the channel geometry on the victim system.

Time-frequency representations (TFRs) have been developed as tools for analysis of non-stationary signals. Signal dependent TFRs are known to perform well for a much wider range of signals than any fixed (signal independent) TFR. This paper describes customised and sequential versions of the signal dependent TFR proposed in [1]. The method, which is based on the use of the Radon transform at distance zero in the ambiguity domain, is simple and effective in dealing with both simulated and real data. The use of the described method for time-scale analysis is also presented. In addition, the paper investigates a simple technique for detection of noisy chirp signals using the Radon transfrom in the ambiguity domain.