ABSTRACT
To meet the increasing demands for remote sensing, a number of radar systems using Linear Frequency Modulation (LFM) waveforms have been deployed, causing the problem of depleting frequency resources. To address this problem, several researchers have proposed the Spectrum Shared Radar System (SSRS) in which multiple radars share the same frequency band to transmit and receive their own signals. To mitigate the interferences caused by the signal transmission by other radars, SSRS employs orthogonal waveforms that inherit the orthogonality of the waveforms from orthogonal codes. However, the inherited orthogonality of the codes is significantly reduced when incorporating LFM waveforms with the codes. To solve this problem, in this paper, we propose a novel but simple scheme for generating a set of optimized coded LFM waveforms via new optimization framework. In the optimization framework, we minimize the weighted sum of autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP) of the coded LFM waveforms to maximize the orthogonality of the waveforms. Through computer simulations, we show that the waveforms generated by the proposed scheme outperform the waveforms created by previous proposals in terms of ASP and CP.
ABSTRACT
Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.
ABSTRACT
A total of 25 college students participated in a study to determine when they would use an air conditioner during a lecture in a university classroom. The ambient temperature and relative humidity were measured 75 cm above the floor every minute. Skin temperatures were measured every minute at seven points, according to the recommendation of Hardy and Dubois. The average clothing insulation value (CLO) of subjects was 0.53 ± 0.07 CLO. The mean air velocity in the classroom was 0.13 ± 0.028 m/s. When the subjects turned the air conditioner both on and off, the average ambient temperatures, relative humidity and mean skin temperatures were 27.4 and 23.7 °C (p = 0.000), 40.9 and 40.0% (p = 0.528) and 32.7 and 32.2 °C (p = 0.024), respectively. When the status of the air conditioner was changed, the differences of skin temperatures in core body parts (head, abdomen and thigh) were not statistically significant. However, in the extremities (mid-lower arm, hand, shin and instep), the differences were statistically significant. Subjects preferred a fluctuating environment to a constant temperature condition. We found that a changing environment does not affect classroom study.
