Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery.

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

3D Defect Localization on Exothermic Faults within Multi-Layered Structures Using Lock-In Thermography: An Experimental and Numerical Approach.

Micro-electronic devices are increasingly incorporating miniature multi-layered integrated architectures. However, the localization of faults in three-dimensional structure remains challenging. This study involved the experimental and numerical estimation of the depth of a thermally active heating source buried in multi-layered silicon wafer architecture by using both phase information from an infrared microscopy and finite element simulation. Infrared images were acquired and real-time processed by a lock-in method. It is well known that the lock-in method can increasingly improve detection performance by enhancing the spatial and thermal resolution of measurements. Operational principle of the lock-in method is discussed, and it is represented that phase shift of the thermal emission from a silicon wafer stacked heat source chip (SSHSC) specimen can provide good metrics for the depth of the heat source buried in SSHSCs. Depth was also estimated by analyzing the transient thermal responses using the coupled electro-thermal simulations. Furthermore, the effects of the volumetric heat source configuration mimicking the 3D through silicon via integration package were investigated. Both the infrared microscopic imaging with the lock-in method and FE simulation were potentially useful for 3D isolation of exothermic faults and their depth estimation for multi-layered structures, especially in packaged semiconductors.

Colloidal InSb Quantum Dots for 1500 nm SWIR Photodetector with Antioxidation of Surface.

III-V quantum dots (QDs) have emerged as significant alternatives to Cd- and Pb-based QDs, garnering notable attention over the past two decades. However, the understanding of III-V QDs, particularly in the short wave-infrared (SWIR) region, remains limited. InAs QDs are widely recognized as the most prominent SWIR QDs, but their absorption beyond 1400 nm presents various challenges. Consequently, InSb QDs with relatively narrower bandgaps have been investigated; however, research on their device applications is lacking. In this study, InSb QDs are synthesized with absorption ranging from 1000 to 1700 nm by introducing Cl- ions to enhance QD surface stability during synthesis. Additionally, it coated InAs and ZnSe shells onto the InSb QDs to validate photoluminescence in the SWIR region and improve photostability. Subsequently, these QDs are employed in the fabrication of photodetector devices, resulting in photodetection above 1500 nm using Pb-free QDs. The photodetection device exhibited an external quantum efficiency (EQE) of 11.4% at 1370 nm and 6.3% at 1520 nm for InSb core QDs, and 4.6% at 1520 nm for InSb/InAs core/shell QDs, marking the successful implementation of such a device. In detail, the 1520 nm for InSb core device showed a dark current density(JD ) value of: 1.46 × 10-9 A/cm2 , responsivity(R): 0.078 A/W, and specific detectivity based on the shot noise(Dsh *): 3.6 × 1012 Jones at 0 V.