ABSTRACT Automatic license plate recognition (ALPR) systems critically depend on the robust and efficient detection of LPs under unconstrained environmental conditions, including significant viewpoint variations and complex backgrounds. To address these challenges, this paper introduces CAD‐Net, a novel corner‐aware LP detection architecture that combines a computationally efficient ResNet‐18 encoder with an efficient multi‐scale feature decoder for accurate LP corner localization. The decoder aggregates and refines features through group dilated convolutions, coordinate attention, and context gated attention, enabling enhanced focus on semantically salient regions while capturing intricate spatial dependencies. The detected LP corner points enable a polygonal region‐of‐interest alignment strategy for geometric rectification of LP features, which is integrated into an end‐to‐end LP recognition framework named CAR‐Net. Comprehensive experiments demonstrate the efficacy of our method. For LP detection, CAD‐Net attains LP detection rates of 99.9% on CCPD‐Base and 100.0% on AOLP‐RP, with a processing speed of 105 frames per second. For end‐to‐end LP recognition, CAR‐Net achieves state‐of‐the‐art performance on multiple benchmarks, CCPD (98.9%), AOLP‐RP (99.2%), PKUdata (98.5%), CLPD (82.3%), and OpenALPR‐BR (99.1%), while maintaining a real‐time inference speed of 72 frames per second. These results confirm practical viability for deployment in real‐world ALPR systems.

Data annotation is extremely difficult due to the satellite imaging conditions, under which the targets are usually small, obscured, and scattered. Therefore, satellite video data annotation inevitably has noise and errors. Moreover, the irregular acceleration, sudden turns, and stops of the target make prediction and trajectory maintenance highly challenging. In this study, we propose the Points of Interest Flows Track (PIFTrack) to address the aforementioned challenges. PIFTrack improves tracking accuracy by modeling target uncertainty distributions and nonlinear motion patterns, while leveraging the spatial inclusion relationships of points of interest (PoIs) across consecutive frames. Specifically, we eliminate the rigid Dirac-based labeling assumption by employing a set of PoIs to model the spatial probability distribution of the target. PoIs enable the model to infer optimal outputs in the vicinity of annotations, thereby improving robustness to annotation errors. Secondly, to capture the real motion transfer patterns of targets in the data, we introduce a diffusion-based ordinary differential equation (ODE) model. Ultimately, we alleviate the impact of tiny object localization drifts on association results by exploiting the inclusion relationship between PoIs. PIFTrack has been extensively evaluated on the VISO, AIR-MOT, CGSTL, and VSMB datasets, exhibiting competitive performance relative to contemporary studies. Our code is open-source and available at https://github.com/softwarePupil/PIFTrack.

Despite promising results in disease detection, breath analysis has not reached its full potential. With the rise of portable gas chromatography (GC) devices and the large volume of data, traditional manual GC peak detection methods are impractical. This work proposes a new approach to chromatographic peak detection which is a necessary step in the GC analysis pipeline. A deep-learning model was trained on simulated breath data to localize the chromatogram peaks and validated on manual annotations. The approach was specifically designed to account for the co-eluted peaks that are often overlooked. The results show that the model outperformed threshold- and derivative-based approaches, as well as other proposed models with high sensitivity and precision.

Automatic license plate recognition plays an important role in intelligent transportation systems and is of great significance. However, at present, most current approaches are only concerned of license plate recognition under restrictive conditions, where the license plates are shot in a frontal view and under good light conditions. These approaches are not robust enough in real-world complex capture scenarios, such as uneven light condition or oblique shooting angle. In order to improve the robustness of recognizing license plates under complex capture scenarios, a robust license plate detection network (CA-CenterNet) is proposed in this paper, together with a segmentation-free network (CNNG) for the recognition of license plate characters. CA-CenterNet can detect not only the center of each license plate, but also four vectors pointing to the four corners of the corresponding license plate, regardless of the rotation and distortion of the license plates, which gives us the possibility to rectify the distorted license plates in the source images. Then, CNNG can accurately identify the characters in the detected license plates without character segmentation. Experimental results prove that our automatic license plate recognition system has good performance in real-world complex capture scenarios and outperforms current license plate recognition models.

Sliding probe methods are designed for the in situ characterization of electrical properties of individual 1-D nanostructures. The key to achieving a high resolution is to keep the contact resistance constant by controlling the contact force and area between the specimen and the sliding probe. We have developed several techniques and tools including differential sliding, flexible probes, and specimen-shape-adaptable probes using nanorobotic manipulation. Compared with conventional methods, these sliding probe methods allow in situ characterization with a higher resolution than conventional methods. Furthermore, they are superior for local property characterization, which is of particular interest for heterostructured nanomaterials and defect detection.

We report the nanorobotic in situ forming and characterization of memristors based on individual copper oxide nanowires (CuO NWs) and their potential applications as nanosensors with memory (memristic sensors or “memsensors”). A series of in situ techniques for the experimental investigations of memristors are developed including nanorobotic manipulation, electro-beam-based forming, and electron energy loss spectroscopy (EELS) enabled correlation of transport properties and carrier distribution. All experimental investigations are performed inside a transmission electron microscope (TEM). The initial CuO NW memristors are formed by localized electron-beam irradiation to generate oxygen vacancies as dopants. Current-voltage properties show distinctive hysteresis characteristics of memristors. The mechanism of such memristic behaviors is explained with an oxygen vacancy migration model. The presence and migration of the oxygen vacancies is identified with EELS. Investigations also reveal that the memristic behavior can be influenced by the deformation of the nanowire, showing that the nanowire memristor can serve as a deformation/force memorable sensor. The CuO NW-based memristors will enrich the binary transition oxide family but hold a simpler and more compact design than the conventional thin-film version. With these advantages, the CuO NW-based memristors will not only facilitate their applications in nanoelectronics but play a unique role in micro-/nano-electromechanical systems (MEMS/NEMS) as well.

Electromigration-based deposition (EMBD) is an additive nanolithography technology for the fabrication of three-dimensional (3D) nanostructures. Key techniques for extending the capability of EMBD have been tackled experimentally including the deposition against a non-conductive surface, shape control of the as-deposited nanostructure, and continuous mass feeding. The process is based on nanofluidic mass delivery at the attogram scale from metal-filled carbon nanotubes (m@CNTs) using nanorobotic manipulation inside a transmission electron microscope. By attaching a conductive probe to the sidewall of the CNT, it has been shown that mass flow can be achieved regardless of the conductivity of the object surface. Experiments have shown the influence of heat sinks on the geometries of the deposits from EMBD. By modulating the relative position between the deposit and the heat sinks using dual probes, it has been possible to reshape the deposits. The limited mass encapsulated inside a CNT requires a frequent change of them for depositing large structures. To realize continuous feeding, a reservoir will be an excellent solution. We have observed that the copper inside the neighbor CNTs to the CNT injector can be sucked into the injector. Although the mechanism is not well understood yet, electromigration and atom-by-atom wall-passing-through may be responsible to this phenomenon. This observation enabled a new path for the design of an EMBD system As a general-purposed nanofabrication process, EMBD will enable a variety of applications such as nanorobotic arc welding and assembly, nanoelectrodes direct writing, and nanoscale metallurgy.