It is well known that the performance of any classification model is effective if the dataset used for the training process and the test process satisfy some specific requirements. In other words, the more the dataset size is large, balanced, and representative, the more one can trust the proposed model's effectiveness and, consequently, the obtained results. Unfortunately, large-size anonymous datasets are generally not publicly available in biomedical applications, especially those dealing with pathological human face images. This concern makes using deep-learning-based approaches challenging to deploy and difficult to reproduce or verify some published results. In this paper, we suggest an efficient method to generate a realistic anonymous synthetic dataset of human faces with the attributes of acne disorders corresponding to three levels of severity (i.e. Mild, Moderate and Severe). Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct levels is considered. To evaluate the performance of the proposed scheme, we consider a CNN-based classification system, trained using the generated synthetic acneic face images and tested using authentic face images. Consequently, we show that an accuracy of 97,6\% is achieved using InceptionResNetv2. As a result, this work allows the scientific community to employ the generated synthetic dataset for any data processing application without restrictions on legal or ethical concerns. Moreover, this approach can also be extended to other applications requiring the generation of synthetic medical images. We can make the code and the generated dataset accessible for the scientific community.

卷积神经网络是解决任意图像分割任务的方式。但是，当图像较大时，内存需求通常超过可用资源，尤其是在普通GPU上。特别是在生物医学成像中，其中3D图像很常见，问题很明显。解决此限制的一种典型方法是通过将图像分为较小的图像贴片将任务分解为较小的子任务。另一种方法（如果适用）是分别查看2D图像部分，并在2D中解决问题。通常，全球环境的丧失使这种方法的有效性降低。当前图像补丁或选定的2D图像部分中可能不存在重要的全局信息。在这里，我们提出了深层神经拼布（DNP），这是一个基于基于补丁的网络的层次结构和嵌套的分割框架，该网络解决了整体上下文和内存限制之间的困境。

随着物联网（IOT）继续增长，确保依赖无线物联网设备的系统的安全性变得严重重要。最近介绍了基于深度学习的被动物理层发射机授权系统，因为它们适应了这些设备的有限计算和电源预算。这些系统已被证明在固定授权发射机集上培训和测试时提供出色的异常检测精度。然而，在实际部署中，由于授权的发射机变化，可能会出现需要添加和删除的发射机。在这种情况下，系统可能会长时间经历，因为培训潜在的深度学习模型通常是耗时的过程。在本文中，我们从信息检索中汲取灵感来解决这个问题：通过利用特征向量作为RF指纹，我们首先证明可以简化培训，以使用当地敏感散列（LSH）将这些特征向量索引到数据库中。然后，我们示出了可以在数据库上执行近似最近的邻居搜索，以执行与深度学习模型的准确性匹配的发射机授权，同时允许更快的再培训超过100倍。此外，在特征向量上使用维度降低技术，以表明我们的技术的授权延迟可以减少以接近基于深度学习的系统的方法。