“Smart Pavement” is an emerging infrastructure for various on-road applications in transportation and road engineering. However, existing road monitoring solutions demand a certain periodic maintenance effort due to battery life limits in the sensor systems. To this end, we present an end-to-end self-powered wireless sensor—ePave—to facilitate smart and autonomous pavements. The ePave system includes a self-power module, an ultra-low-power sensor system, a wireless transmission module and a built-in power management module. First, we performed an empirical study to characterize the piezoelectric module in order to optimize energy-harvesting efficiency. Second, we developed an integrated sensor system with the optimized energy harvester. An adaptive power knob is designated to adjust the power consumption according to energy budgeting. Finally, we intensively evaluated the ePave system in real-world applications to examine the system’s performance and explore the trade-off.

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect hand approaching behaviors by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of hand movements to the vibration of backscattered tag signals. APID is compatible with commodity off- the shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID a commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios. Extensive evaluations demonstrate that APID can achieve high detection accuracy in both scenarios.

Among the foremost contributors to compromised driving safety is the abrupt emergence of obstacles or pedestrians within drivers’ non-line-of-sight regions. Previous investigations into non-line-of-sight imaging have predominantly depended on costly apparatus or have been confined to controlled laboratory settings (e.g., extensive planar reflectors and regulated illumination). Consequently, these technological approaches prove impractical within intricate driving environments. In this paper, we introduce a shadow based non-line-of-sight moving obstacle detection system devised to augment Advanced Driver Assistance Systems (ADAS), ensuring adequate time for safe response and halting. Our approach incorporates a shadow signal discriminator tailored to evaluate faint shadows generated by moving obstacles, such as pedestrians within blind spots. Note that we merely use commercial onboard sensors and our system is robust to various lighting scenarios and planar reflectors. We comprehensively assess our methodology's adaptability by employing datasets acquired from real-world driving scenarios encompassing diverse road surfaces and lighting conditions. The results substantiate the system's efficacy in detecting pedestrians in motion within NLOS regions, showcasing an impressive detection range of 22 meters. This proficiency enables the system to pre-emptively forewarn the ADAS, facilitating the maintenance of a safe distance from the pedestrian.