Convolutional neural network for bio-medical image segmentation with hardware acceleration

Application of artificial intelligence in Bio-Medical image processing is gaining more and more importance in the field of Medical Science. The bio medical images, has to go through several steps before the diagnosis of the disease. Firstly, the images has to be acquired and preprocessing has to be done and the data has to be stored in memory. It requires huge amount of memory and processing time. Among the preprocessing steps, edge detection is one of the major step. Edge detection filters the unwanted details in the image, and preserves the edges of the image, which describe the boundary of the image. In biomedical application, for the detection of the diseases, it is very essential to have the boundary detail of the acquired image of the organ under observation. Thus it is very essential to extract the edges of the images. Power is one of the main parameters that have to be considered while dealing with biomedical instruments. The biomedical signal processing instruments should be capable of operating at low power and also at high speed. In order to segregate the images into different levels or stage, we use convolutional neural networks for classification. By having a hardware architecture for image edge detection, the computational time for pre-processing of the image can be reduced, and the hardware can be a part of acquisition device itself. In this paper a low-power architecture for edge detection to detect the biomedical images are presented. The edge detection output are given to the system, which will diagnose the diseases using image classification using convolutional neural network. In this paper, Sobel and Prewitt, algorithms are used for edge detection using 180 nm technology. The edge detection algorithms are implemented using VLSI, and digital IC design of the architecture is presented. The algorithms for edge detection is co-simulated using MATLAB and Modelsim. The architecture is first simulated using CMOS logic and new method using domino logic is presented for low power consumption.     

Research and application of virtual user context information security strategy based on group intelligent computing

This article first introduced the current technology of the privacy protection model, and analyzed their characteristics and deficiencies. Afterwards, from the point of view of revenue, the shortcomings of the traditional privacy protection model have analyzed through the group intelligent computing method. Based on this, this paper proposes a research and application of virtual user information of security strategy based on group intelligent computing, through the collection of visitor's private information historical access data, intelligent calculation of the strategy group between the visitor and the interviewee. The setting of the threshold of the visited person can protect the privacy information of the user more effectively. In this paper, the implementation flow, algorithm implementation process, and specific architecture design of the proposed virtual user of privacy protection model based on group intelligent computing are introduced respectively. The specific algorithms include PCA, BP neural network, and genetic algorithm. Finally, the proposed privacy has verified through experiments. Protection model can protect user privacy more effectively than traditional privacy protection model. In the future, we will further expand and improve the privacy protection model of virtual users based on group intelligent computing, including considering the dynamic and inconsistency of access to the privacy information, that is, accessing different private information will produce different overlay effects and parallelism. We will also study how to apply this model to actual systems such as shopping websites and social platforms, and use commercial data to evaluate the performance of the model and further improve it.     

Wild facial expression recognition based on incremental active learning

Facial expression recognition in a wild situation is a challenging problem in computer vision research due to different circumstances, such as pose dissimilarity, age, lighting conditions, occlusions, etc. Numerous methods, such as point tracking, piecewise affine transformation, compact Euclidean space, modified local directional pattern, and dictionary-based component separation have been applied to solve this problem. In this paper, we have proposed a deep learning–based automatic wild facial expression recognition system where we have implemented an incremental active learning framework using the VGG16 model developed by the Visual Geometry Group. We have gathered a large amount of unlabeled facial expression data from Intelligent Technology Lab (ITLab) members at Inha University, Republic of Korea, to train our incremental active learning framework. We have collected these data under five different lighting conditions: good lighting, average lighting, close to the camera, far from the camera, and natural lighting and with seven facial expressions: happy, disgusted, sad, angry, surprised, fear, and neutral. Our facial recognition framework has been adapted from a multi-task cascaded convolutional network detector. Repeating the entire process helps obtain better performance. Our experimental results have demonstrated that incremental active learning improves the starting baseline accuracy from 63% to average 88% on ITLab dataset on wild environment. We also present extensive results on face expression benchmark such as Extended Cohn-Kanade Dataset, as well as ITLab face dataset captured in wild environment and obtained better performance than state-of-the-art approaches.     

社会交流中人际神经同步的认知机制

近年来,许多研究者开始关注社会交流中的人际神经同步机制,并将人际神经同步作为研究社会交流的一个神经指标,这对于揭示社会交流的本质和规律具有重要意义。本文从心理理论和镜像神经系统的角度,分析社会交流中神经同步的认知机制及其影响因素。未来的研究应关注这两套机制是否因交流目的、对象、形式或内容的不同,而在不同的脑区表现出神经同步,进而引发了不同认知机制的争议;以及这两套机制各自或协同工作适用的情景和任务。     

文化差异视角下个体合作行为的产生、影响机制建构及认知神经基础

合作行为是指个体或群体之间为了实现共同的目标和利益而进行的协同行为或意向。本文基于行为-认知-大脑的三重映射关系,对合作行为的文化差异性进行了深入阐释,并在此基础上构建了文化影响合作行为的社会认知中介模型;未来研究可从实证视角对合作行为文化差异的社会认知内容、认知神经机制等方面进行验证、挖掘和改善。     

超扫描视角下的社会互动脑机制

超扫描技术可同时记录多名被试在同一认知活动中的脑活动,并通过分析脑间活动同步及其与行为指标间的关系描述社会互动的群体脑机制。本文总结了近十多年超扫描研究在合作与竞争、动作和行为同步、人际交流等领域的成果,在已有研究基础上指出脑间活动同步可刻画社会互动中感觉运动、思维决策以及信息传递等三大层面上的互动情况,可能成为社会互动活动的神经标记,并阐述了超扫描技术研究的局限性及其研究展望与应用潜能。     

语音加工的发展、老化及其神经机制

作为人类语言活动的重要组成成分,语音加工经历着“习得-发展-老化”的变化过程。本文首先综述了婴儿、儿童、成年人及老年人在语音意识、语音提取,语音工作记忆等方面的表现及相应加工机制。其次,分析了不同年龄段个体语音加工发展变化的神经基础。最后,通过比较不同时期个体语音加工发展进程,围绕语音加工发展过程、语音老化有无关键年龄以及如何干预等问题进行展望。     

Linking patterns of infant eye movements to a neural network model of the ventral stream using representational similarity analysis

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N = 54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12 months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is     

人们为什么会羞怯：认知机制及神经基础

羞怯指个体在社交情境下的抑制, 是个体参与社交的阻碍。近年来, 关于羞怯的认知神经科学研究增多, 学者基于元认知模型、社会适应模型、侧化脑-体情绪模型等理论, 探讨了大脑结构和功能以及几种与感知觉和注意相关的ERP成分与羞怯的关系。但当前关于羞怯认知神经科学的理论和实证研究尚处于起步阶段。基于上述提出羞怯的心理发展模型; 未来应从人格和情绪双角度研究羞怯, 并在开发研究范式的基础上, 加大羞怯认知神经机制的探讨。     

模型方法在神经干细胞研究中的应用

神经干细胞(neural stem cell NSC)是一种终身具有自我更新能力的细胞,其子细胞能分化产生神经系统的各类细胞,这一特性为神经系统各类疾病的治疗带来了新的希望.模型方法是医学研究的基本方法.就模型方法在神经干细胞研究中的应用作一综述有重要意义.