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The secure transmission of data within a network has received great attention. As the core of the security management mechanism, the key management scheme design needs further research. In view of the safety and energy consumption problems in recent papers, we propose a key management scheme based on the pairing-free identity based digital signature (PF-IBS) algorithm for heterogeneous wireless sensor networks (HWSNs). Our scheme uses the PF-IBS algorithm to complete message authentication, which is safer and more energy efficient than some recent schemes. Moreover, we use the base station (BS) as the processing center for the huge data in the network, thereby saving network energy consumption and improving the network life cycle. Finally, we indirectly prevent the attacker from capturing relay nodes that upload data between clusters in the network (some cluster head nodes cannot communicate directly). Through performance evaluation, the scheme we proposed reasonably sacrifices part of the storage space in exchange for entire network security while saving energy consumption.

Computer-aided diagnosis of retinopathy is a research hotspot in the field of medical image classification. Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common ocular diseases that can result in partial or complete loss of vision. Optical coherence tomography imaging (OCT) is widely applied to the diagnosis of ocular diseases including DME and AMD. In this paper, an automatic method based on deep learning is proposed to detect AME and AMD lesions, in which two publicly available OCT datasets of retina were adopted and a network model with effective feature of reuse feature was applied to solve the problem of small datasets and enhance the adaptation to the difference of different datasets of the approach. Several network models with effective feature of reusable feature were compared and the transfer learning on networks with pretrained models was realized. CliqueNet achieves better, classification results compared with other network models with a more than 0.98 accuracy and 0.99 of area under the curve (AUC) value finally.

The surface electric field induced by external geomagnetic source fields is modeled for a continental‐scale 3‐D electrical conductivity model of Australia at periods of a few minutes to a few hours. The amplitude and orientation of the induced electric field at periods of 360 s and 1800 s are presented and compared to those derived from a simplified ocean‐continent (OC) electrical conductivity model. It is found that the induced electric field in the Australian region is distorted by the heterogeneous continental electrical conductivity structures and surrounding oceans. On the northern coastlines, the induced electric field is decreased relative to the simple OC model due to a reduced conductivity contrast between the seas and the enhanced conductivity structures inland. In central Australia, the induced electric field is less distorted with respect to the OC model as the location is remote from the oceans, but inland crustal high‐conductivity anomalies are the major source of distortion of the induced electric field. In the west of the continent, the lower conductivity of the Western Australia Craton increases the conductivity contrast between the deeper oceans and land and significantly enhances the induced electric field. Generally, the induced electric field in southern Australia, south of latitude −20°, is higher compared to northern Australia. This paper provides a regional indicator of geomagnetic induction hazards across Australia.