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    為營造學院良好的學術環境氛圍，本周將舉辦學術交流報告會，供師生和學生之間相互交流討論，具體安排如下。

    日期：2020年11月26日（周四）

    時間：15：00（下午三點）

    地點：理科樓B311

    匯報人：18級張文斌

    論文題目：

    《Reusable Mesh Signature Scheme for Protecting Identity Privacy of IoT Devices》

論文簡介：

The development of the Internet of Things (IoT) plays a very important role for processing

data at the edge of a network. Therefore, it is very important to protect the privacy of IoT devices when these devices process and transfer data. A mesh signature (MS) is a useful cryptographic tool, which makes a signer sign any message anonymously. As a result, the signer can hide his specific identity information to the mesh signature, namely his identifying information (such as personal public key) may be hidden to a list of tuples that consist of public key and message. Therefore, we propose an improved mesh signature scheme for IoT devices in this paper. The IoT devices seen as the signers may sign their publishing data through our proposed mesh signature scheme, and their specific identities can be hidden to a list of possible signers. Additionally, mesh signature consists of some atomic signatures, where the atomic signatures can be reusable. Therefore, for a large amount of data published by the IoT devices, the atomic signatures on the same data

can be reusable so as to decrease the number of signatures generated by the IoT devices in our proposed scheme. Compared with the original mesh signature scheme, the proposed scheme has less computational costs on generating final mesh signature and signature verification. Since atomic signatures are reusable, the proposed scheme has more advantages on generating final mesh signature by reconstructing atomic signatures. Furthermore, according to our experiment, when the proposed scheme generates a mesh signature on 10 MB message, the memory consumption is only about 200 KB. Therefore, it is feasible that the proposed scheme is used to protect the identity privacy of IoT devices.

錄取期刊：Sensors.

匯報人：18級張成文

論文題目：

《A SAR Image Target Recognition Approach via Novel SSF-Net Models》

論文簡介：

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. +e experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1.

錄用期刊：Mobile Information Systems | Hindawi

論文題目：

《High Resolution Radar Targets Recognition via Inception-Based VGG (IVGG) Networks》

論文簡介：

Aiming at High Resolution Radar target recognition, new convolutional neural networks, namely Inception-Based VGG (IVGG) Networks, are proposed to classify and recognize different targets in High Range Resolution Profile (HRRP) and Synthetic Aperture Radar (SAR) signals. The IVGG networks have been improved in two aspects. One is to adjust the connection mode of the full connection layer. The other is to introduce Inception module into the Visual Geometry Group (VGG) network to make the network structure more suitable for radar target recognition. After Inception module, we also add a point convolutional layer to strengthen the nonlinear of the network. Compared with VGG network, IVGG networks are simpler and have fewer parameters. The experiments are compared with GoogLeNet, ResNet18, DenseNet121 and VGG on 4 datasets. The experimental results show that the IVGG networks have better accuracies than those of the existing convolutional neural networks.

錄用期刊：Computational Intelligence and Neuroscience | Hindawi