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Long-range target geolocalization in outdoor complex environments has been a long-term challenge in intelligent transportation and autonomous vehicles with great interest in fields of vehicle detection, monitoring, and security. However, since traditional monocular or binocular geolocalization methods are typically implemented by depth estimation or parallax computation, suffering from large errors when targets are far away, and thus hard to be directly applied to outdoor environments. In this paper, we propose a visual servo-based global geolocalization system, namely VSG, which takes the target position information in the binocular camera images as the control signals, automatically solves the global positions according to the gimbal rotation angles. This system solves the problem of long-range static and dynamic target geolocalization (ranging from 220 m to 1200 m), and localizes the farthest target of 1223.8 m with only 3.5$\%$ localization error. VSG also realizes full-process automation by combining the deep learning-based objection detection, and its localization performance has been proved by series of experiments. This system is the longest-range global geolocalization method with preferred accuracy reported so far, and can be deployed in different geomorphology with great robustness.

The accurate assessment of surface roughness is critical for numerous applications ranging from quality control in manufacturing to material characterization. To achieve a functional capability of roughness perception, a flexible pressure sensor based on polyvinylidene fluoride-Ti3C2 (PVDF/MXene) nanocomposite is developed. The sensor consists of electrospun PVDF nanofibers embedded with 2-D MXene nanosheets. The MXene enhances the piezoelectric $\beta $ -phase content of the PVDF up to 97.2% at optimal loading of 2.5 wt%. The PVDF/MXene nanocomposite exhibited high piezoelectric voltage sensitivity up to 0.059 V kPa $^{-{1}}$ under applied pressures. The wavelet transform analysis of signals obtained by scanning the sensor on sandpapers of varying roughness showed distinct time–frequency patterns corresponding to different surface roughness levels. Unsupervised dimensionality reduction using t-distributed stochastic neighbor embedding (t-SNE) revealed clustering of roughness data into distinct categories. A convolutional neural network (CNN) classifier achieved 98% accuracy in categorizing the surface roughness based on the sensor signal wavelet transforms. The piezoelectric nanocomposite sensor shows promise for surface metrology applications.

Advances in wireless sensors, biomedical devices, and micro-robotics exert more pressure on creating reliable, miniaturized, and self-sustained energy supply solutions for these micro-electromechanical systems. Rotational energy harvesting (REH) is one of the rapidly growing areas for self-powered electronics using available rotational energy or energy converted from other sources in the environment. This paper comprehensively reviews the state-of-the-art progress in REH in terms of the available energy characteristics, harvester categories, adopted methodologies and mechanisms, and promising applications. Unique mechanisms and methodologies, such as using gravity and centrifugal force combined with other nonlinear mechanisms, are discussed and characterized. In terms of applications, wearable and implantable devices, automotive, rotating machines, renewable energy systems, and environmental sensing are discussed and reviewed to illustrate how rotational energy harvesters have been developed and adopted accordingly. Based on progress to date, the key developments, critical challenges, and issues are summarized and discussed. Moving forward, an outlook is presented to outline potential research directions and opportunities in this area.

Purpose: Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method. Design/methodology/approach: To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method. Findings: Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle. Originality/value: This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

Rotational energy is widely distributed or easily acquirable from other energy sources (fluid flow, machine operation or human motion) in many industrial and domestic scenarios. At small scales, power generation from such rotational ambient sources can enable many autonomous and self‐reliant sensing applications. In this paper, three typical types of micro‐generators (energy harvesters), namely electromagnetic (EMREHs), piezoelectric resonant (PRREHs) and piezoelectric non‐resonant rotational energy harvesters (PNRREHs) are discussed and compared in terms of device dimensions and operation frequencies. Theoretical models are established for each type to calculate maximum achievable output power as a function of device dimension and operating frequency. Using these theoretical models, scaling laws are established for each type to estimate the achievable output. The EMREHs have a strong scaling effect both on device dimension (as L5) and on operating frequency (as ω2), whereas the PNRREHs are less so (L2.5ω0.5). PRREHs have a narrow band‐width as resonant harvesters, and are ideal for cases where the excitation frequency is constant. This study provides a guideline for selection and design of rotational energy harvesters (REH) when the device dimension and operating frequency are defined. The proposed scaling laws offer a convenient method to estimate the harvester performance for different dimensions and operating frequencies. Three key types of micro‐generators (energy harvesters), namely electromagnetic (EMREHs), piezoelectric resonant (PRREHs) and piezoelectric non‐resonant rotational energy harvesters (PNRREHs) are discussed and compared in terms of device dimensions and operation frequencies. Theoretical models are established to calculate maximum achievable output power as a function of device dimension and operating frequency. Scaling laws are established for each type based on the theoretical models. The EMREHs have a strong scaling effect both on device dimension (as L5) and on operating frequency (as ω2), whereas the PNRREHs are less so (L2.5ω0.5). PRREHs are narrowband and ideal for constant excitation frequency cases. This study provides a guideline for selection and design of rotational energy harvesters for different device dimensions and operating frequencies.

Energy harvesting technology can provide a power solution for IoT sensors. In this paper, a sequentially-excited multi-oscillator piezoelectric rotary energy harvester (SEMPREH) is proposed. A theoretical model of the piezoelectric oscillator was established, and a magnetic simulation analysis was performed. The results show that for the same exciting magnet arrangement (A) and rotation speed, the voltages when the exciting and excited magnets in attraction are higher than in repulsion. The waveforms are denser and have a higher charging capacity with A4compared to A1(simultaneous excitation). The optimum combination is attraction and A4with a maximum peak-to-peak open-circuit voltage of 23.2 V at 200 RPM, and the maximum average power at the same speed is 1.11 mW (at 30 kΩ). The SEMPREH assembled with the wind turbine can light up the LED board at 4.13 m/s. It has the potential to power microelectromechanical systems such as IoT sensors.