Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi‐level Cellular Neural Networks

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi‐level cellular neural network (ML‐CNN) is introduced, developed, and applied to real data. ML‐CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas‐Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.     

A Review of FPGA-Based Custom Computing Architecture for Convolutional Neural Network Inference

Convolutional neural network (CNN) has been widely adopted in many tasks. Its inference process is usually applied on edge devices where the computing resources and power consumption are limited. At present, the performance of general processors cannot meet the requirement for CNN models with high computation complexity and large number of pa-rameters. Field-programmable gate array (FPGA)-based custom computing architecture is a promising solution to further enhance the CNN inference performance. The software/hardware co-design can effectively reduce the computing overhead, and improve the inference performance while ensuring accuracy. In this paper, the mainstream methods of CNN structure design, hardware-oriented model compression and FPGA-based custom architecture design are summarized, and the improvement of CNN inference performance is demonstrated through an example. Challenges and possible research directions in the future are concluded to foster research efforts in this domain.     

Micro-expression recognition using advanced genetic algorithm

In recent years, numerous facial expression recognition related applications had been commercialized in the market. Many of them achieved promising and reliable performance results in real-world applications. In contrast, the automated micro-expression recognition system relevant research analysis is still greatly lacking. This is because of the nature of the micro-expression that is usually appeared with relatively lesser duration and lower intensity. However, due to its uncontrollable, subtlety, and spontaneity properties, it is capable to reveal one’s concealed genuine feelings. Therefore, this paper attempts to improve the performance of current micro-expression recognition systems by introducing an efficient and effective algorithm. Particularly, we employ genetic algorithms (GA) to discover an optimal solution in order to facilitate the computational process in producing better recognition results. Prior to the GA implementation, the benchmark preprocessing method and feature extractors are directly adopted herein. Succinctly, the complete proposed framework composes three main steps: the apex frame acquisition, optical flow approximation, and feature extraction with CNN architecture. Experiments are conducted on the composite dataset that is made up of three publicly available databases, viz, CASME II, SMIC, and SAMM. The recognition performance tends to prevail the state-of-the-art methods by attaining an accuracy of 85.9% and F1-score of 83.7%.     

基于CNN的SAR车辆目标检测

传统的SAR目标检测算法容易受到复杂背景的干扰,因此利用被广泛应用于图像目标检测和识别领域的Faster-RCNN方法,对复杂背景下的SAR图像进行车辆目标检测实验。在对样本数据进行预处理后对车辆真实位置进行标记,采用可视化的深度学习客户端对样本进行裁剪和旋转,扩充样本数据库。利用已充分训练的模型权重对ZF和VGG-16网络进行预训练,再利用扩充的数据集进行训练和验证,并使用包含MiniSAR数据的测试集进行测试。实验证明,ZF网络和VGG-16的检测效果类似,但是ZF网络因为网络层数更少因而检测耗时更短。     

眼睛运动模型的设计

现阶段,生物模式识别系统主要有两种实现方案,且这些方案的实现都与眼睛的运动有关,因此这里利用细胞神经网络（CNN）,基于欧氏距离的质心（COG）搜索算法以及距离权重选择模型（RSDW）的思想建立了一个眼睛运动模型。该模型输出的眼睛运动轨迹图特性与实际仪器测试的眼睛运动轨迹图特性具有较好的一致性,同时该模型能直接作为生物模式识别系统中信息获取模块的重要组成部分。     

基于CNN池化和进化策略的一般神经网络图像分类研究

网络的爆炸式发展产生了海量的图像,图像标签的错误和缺失比较常见,图像分类研究很有必要。CNN池化能够提取到输入矩阵的重要特征,降低数据的维度。进化策略是模仿生物"优胜劣汰"进化方式的一种启发式算法,能快速找到问题的解。本文基于CNN池化提取一组有正确标签的图像的特征,搭建层数为3的神经网络,进化策略优化初始权重,通过训练集训练分类模型,通过测试集来验证模型的优劣,并使最终的模型实现对未知类别图像的高效分类。实例验证阶段收集10类100张犬类图片,按照各研发步骤进行实验,算法结果验证了进化策略优化权重的必要及神经网络模型的高效。     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.     

基于CTCNet对球结膜进行糖尿病视网膜病变分类

糖尿病视网膜病变(diabetic retinopathy, DR)是一种糖尿病性微血管病变,会在球结膜微血管上有所体现,球结膜血管图像的获取比眼底图像更加便捷,但微血管的特征变化微小且难以量化。为了能够对患者进行早期辅助诊断,本文依据球结膜微血管形态与DR的关联,首先对球结膜图像进行预处理,使用限制对比度自适应直方图均衡(contrast limited adaptive histogram equalization, CLAHE)算法进行图像增强,随机处理使数据增强,然后结合卷积神经网络(convolutional neural network, CNN)和Transformer各自的网络优势构建CTCNet,对处理后的球结膜血管图像进行DR分类,分类准确率达到了97.44%,敏感度97.69%,特异性97.11%,精确度97.69%,通过实验对比CNN和Transformer, CTCNet网络性能优于其他模型,能够有效识别DR。     

Steganography using a 3-player game

Image steganography aims to securely embed secret information into cover images. Until now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD, were among the most secure and most often used methods for image steganography. With the arrival of deep learning and more specifically, Generative Adversarial Networks (GAN), new steganography techniques have appeared. Among them is the 3-player game approach, where three networks compete against each other. In this paper, we propose three different architectures based on the 3-player game. The first architecture is proposed as a rigorous alternative to two recent publications. The second takes into account stego noise power. Finally, our third architecture enriches the second one with a better interaction between embedding and extracting networks. Our method achieves better results compared to existing works Hayes and Danezis (2017), Zhu et al. (2018), and paves the way for future research on this topic.     

Understanding convolutional neural networks with a mathematical model

This work attempts to address two fundamental questions about the structure of the convolutional neural networks (CNN): (1) why a nonlinear activation function is essential at the filter output of all intermediate layers? (2) what is the advantage of the two-layer cascade system over the one-layer system? A mathematical model called the “REctified-COrrelations on a Sphere” (RECOS) is proposed to answer these two questions. After the CNN training process, the converged filter weights define a set of anchor vectors in the RECOS model. Anchor vectors represent the frequently occurring patterns (or the spectral components). The necessity of rectification is explained using the RECOS model. Then, the behavior of a two-layer RECOS system is analyzed and compared with its one-layer counterpart. The LeNet-5 and the MNIST dataset are used to illustrate discussion points. Finally, the RECOS model is generalized to a multilayer system with the AlexNet as an example.