全方位空间自闭症儿童眼动信号处理与分析

针对以往自闭症（autism spectrum disorder, ASD）眼动研究无法展现真实全方位世界中自闭症患者的视觉注意的问题,本文提出应用虚拟现实（virtual reality, VR）技术和360°全景图像,在VR环境下收集眼动和头动数据,以分析和模拟自闭症儿童在真实全方位世界中的视觉注意。文中建立了首个大规模的全景图像ASD眼动数据集,改进了三层显著性计算模型的所考虑图像特征及其提取方法,量化了数据集中所有全景图像的特征,使用支持向量机（support vector machine, SVM）计算了不同特征对视觉注意力分配的影响程度。进而,我们从对图像中不同特征的视觉关注度,到头眼运动的区别与联系,进行了对自闭症儿童与对照组视觉注意的定性与定量比较,得到了自闭症儿童非典型视觉注意的特征。这项研究有助于分析自闭症的视觉特征,可以进一步帮助辅助自闭症的分类、诊断和预后康复。      

Learning bayesian classifiers for scene classification with a visual grammar

A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.     

Subclass linear discriminant analysis for video-based face recognition

We present a novel subclass Linear Discriminant Analysis algorithm for feature extraction that copes with the severe pose, expression and illumination changes present in faces extracted from far-field video streams with subjects unconstrained in their motion and uncooperative to the system. Our novelty lies on the efficient automatic generation of subclasses from the gallery faces, by exploiting their different visual appearance and not constrained by their numbers per class. The proposed feature extraction algorithm is integrated in our complete face recognition system, with modules for preprocessing, classification, and decision fusion. We demonstrate the capability of the new algorithm to automatically generate discriminable subclasses and the resulting improved classification accuracy on a challenging video-based dataset, comprising low quality and resolution faces, as well as large variations in visual appearance. Our results indicate superior recognition rate compared to any systems in the CLEAR 2007 evaluation, running on that dataset.     

An experimental study on the universality of visual vocabularies

Bag-of-visual-words has been shown to be a powerful image representation and attained success in many computer vision and pattern recognition applications. Usually for a given classification task, researchers choose to build a specific visual vocabulary, and the problem of building a universal visual vocabulary is rarely addressed. In this paper we conduct extensive classification experiments with three features on four image datasets and show that the visual vocabularies built from different datasets can be exchanged without apparent performance loss. Furthermore, we investigate the correlation between the visual vocabularies built from different datasets and find that they are nearly identical, which explains why they are universal across classification tasks. We believe that this work reveals what is behind the universality of visual vocabularies and narrows the gap between bag-of-visual-words and bag-of-words in text domain.     

连续闪烁抑制范式实验平台设计及其效果检测

连续闪烁抑制范式(continuous flash suppression paradigm, CFS)是当前最新的意识下视觉感知研究范式。然而,由于目前尚缺乏成熟的可视化的CFS范式视觉实验平台,严重制约了相关研究的进展。鉴于此,本文采用标准化精密光学元件搭建了CFS范式实验硬件系统,利用MATLAB下的Psychtoolbox工具箱研制了可视化CFS范式实验软件系统,成功试制了CFS范式视觉实验平台。其中,实验软件系统不仅提供了功能完备、设置方便的实验参数可视化设置界面,而且开发并集成了具备不同物理特性的蒙德里安掩蔽图片自动生成程序。为验证CFS范式视觉实验平台的性能,本文设计并开展了图片和视频刺激的意识下视觉感知实验,展现了实验平台良好的工作性能。总体而言,视觉实验平台实验设计方便、掩蔽效果良好、程序运行稳定、数据存取便捷,可以更好地服务于CFS相关心理物理学实验的设计与开展,有效提升无意识视觉认知研究的水平。     

Active perception for autonomous sensor systems

We have considered the problem of active perception interacting with a complex environment. The concept of perception provides a different means of awareness in an artificial system; it offers a classification process of the relevant features of an occurrence in the environment. We used a multidisciplinary perspective to show three active perception techniques: Direct perception, Sequential perception, and Direct perception with memory capabilities. The three case studies show that the way a system perceives the environment is highly dependent on the time sequence for the sensations and perceptions. Currently, we have developed the perception capabilities and the individual sensors in an artificial system. We are now in the process of fully integrating the perception concept from these three examples into a general platform.     

基于VxWorks的飞机电气处理机多任务程序设计

固态配电系统核心处理机也称作电气系统处理机（PSP）,是固态配电系统的控制中心。阐述了在VxWorks下飞机固态配电核心处理机的多任务程序设计流程,对多任务进行了细致的划分和分析,并且针对双机热备份的功能进行了深入的研究。     

The application of time-frequency methods to the analysis ofpostural sway

We apply time-frequency (TF) spectral analysis techniques, namely evolutionary spectral estimators, to postural sway data gathered during quiet standing and in response to external visual stimuli. These techniques provide insight into the time-varying properties of the human balance control systems during standing. We demonstrate by means of individual and group examples that the results of the TF methods can be used to characterize the behavior of the balance system for groups of patients and controls. Specifically we show that, for healthy control subjects, sway at a visual stimulus frequency toward and away from the subject shows an amplitude which decays in time. On the other hand, patients display a response whose amplitude at the stimulus frequency increases with time. Thus TF analysis yields insights into the time-varying nature of the postural control system     

基于R16的五子棋虚拟现实对弈系统设计

基于R16芯片和IOIO模块开发了一套五子棋虚拟现实对弈平台。平台基于IOIO模块连接图像识别模块、落子系统模块、远程通信模块,开发了针对五子棋棋盘的图像识别软件系统和机械落子系统,实现了虚拟现实对弈功能,包括人机对弈和远程对弈两方面子功能。人机对弈功能通过图像识别分析棋盘信息并作出决策,之后控制机械结构落子。远程对弈功能根据远程棋手决策指导机械机构完成落子,并再次采集棋盘信息反馈给远程棋手。     

Classification of plasma cortisol patterns in normal subjects andin Cushing''s syndrome

The 24-h pattern of half-hourly sampled plasma cortisol in normal human subjects shows a 24-h (circadian) period, which may be variably distorted in patients who suffer from autonomous hypercortisolism (Cushing's syndrome). We have developed a pattern recognition system for computer classification of cortisol time series into the normal class and subclasses of Cushing's syndrome with different etiology ("pituitary" designating pituitary tumor, "adrenal" designating adrenal tumor, and "ectopic" designating tumor elsewhere). Discriminatory features were extracted from Fourier analysis and Karhunen-Loeve expansion coefficients of cortisol time series. Decision functions were trained by the LMSE algorithm and tested by the jack-knife test procedure on a data-base of 90 normal and patient patterns. The classification accuracy for normal, "pituitary," "adrenal," and "ectopic" classes was 100, 98.1, 98.3, and 100%, respectively. Hence this pattern recognition system may be useful as an aid in the differential diagnosis of Cushing's syndrome.     

基于LabVIEW的远程温度检测控制系统设计与实现

环境温度是测控设备稳定工作的重要指标,对其及时、准确地掌控显得十分重要.针对目前测控任务重、参试人员少特点,以LabVIEW可视化图形编程开发环境为平台,结合简单的硬件电路,设计出一个实时性较好的远程温度检测控制系统,为一些无人值守设备营造良好的工作环境,并通过模拟和仿真试验,验证了其有效可行.     

Vanadium Core–Shell Nanorods Inspect Metabolic Changes of Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and ranks as the fifth leading cause of visual impairment, but an understanding of DR development has been hampered by the lack of an efficient metabolomic tool. Herein, vanadium core–shell nanorods are developed for metabolic fingerprinting to probe molecular variation in DR. First, a series of vanadium core–shells are constructed with different elemental composition and structural parameters, using silica nanorods to support vanadium oxide. The plasma metabolic fingerprints (MFs) are extracted by the optimized vanadium core–shell nanorod‐assisted laser desorption/ionization mass spectrometry, by analyzing 500 nL of native plasma in seconds. As a result, DR patients are differentiated from non DR controls with a sensitivity of 94% and specificity of 90% using a classification model built on the plasma MFs. Furthermore, DR progression is monitored by a panel of plasma metabolic signatures with gradual changes. This work provides an advanced molecular tool for the metabolomic characterization of DR and may guide the clinical decision making in DR for personalized medicine in the future.     

Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing.     

基于LabVIEW的VFD视觉检测技术研究

设计了一种视觉检测系统。该系统由CCD摄像机、计算机硬件和 LabVIEW 软件平台两部分构成,计算机控制摄像机采集标准VFD图片作为模板,将所需测试的VFD图片进行采集并与之进行比较,完成确定VFD缺陷个数和缺陷位置的判断检测,并具有实时显示、保存和回放功能。由此,大幅减轻了检测人员的检测难度,加快了检测速度,且提高了检测效率。     

Force controlled and teleoperated endoscopic grasper for minimallyinvasive surgery-experimental performance evaluation

Minimally invasive surgery generates new user interfaces which create visual and haptic distortion when compared to traditional surgery. In order to regain the tactile and kinesthetic information that is lost, a computerized force feedback endoscopic surgical grasper (FREG) was developed with computer control and a haptic user interface. The system uses standard unmodified grasper shafts and tips. The FREG can control grasping forces either by surgeon teleoperation control, or under software control. The FREG performance was evaluated using an automated palpation function (programmed series of compressions) in which the grasper measures mechanical properties of the grasped materials. The material parameters obtained from measurements showed the ability of the FREG to discriminate between different types of normal soft tissues (small bowel, lung, spleen, liver, colon, and stomach) and different kinds of artificial soft tissue replication materials (latex/silicone) for simulation purposes. In addition, subjective tests of ranking stiffness of silicone materials using the FREG teleoperation mode showed significant improvement in the performance compared to the standard endoscopic grasper. Moreover, the FREG performance was closer to the performance of the human hand than the standard endoscopic grasper. The FREG as a tool incorporating the force feedback teleoperation technology may provide the basis for application in telesurgery, clinical endoscopic surgery, surgical training, and research.     

Optimization of symbolic feature extraction for pattern classification

The concept of symbolic dynamics has been used in recent literature for feature extraction from time series data for pattern classification. The two primary steps of this technique are partitioning of time series to optimally generate symbol sequences and subsequently modeling of state machines from such symbol sequences. The latter step has been widely investigated and reported in the literature. However, for optimal feature extraction, the first step needs to be further explored. The paper addresses this issue and proposes a data partitioning procedure to extract low-dimensional features from time series while optimizing the class separability. The proposed procedure has been validated on two examples: (i) parameter identification in a Duffing system and (ii) classification of fatigue damage in mechanical structures, made of polycrystalline alloys. In each case, the classification performance of the proposed data partitioning method is compared with those of two other classical data partitioning methods, namely uniform partitioning (UP) and maximum entropy partitioning (MEP).     

Bioinspired Triboelectric Soft Robot Driven by Mechanical Energy

A sustainable power source is a key technical challenge for practical applications of electrically responsive soft robots, especially the required voltage is over several thousand volts. Here, a practicable new technology, triboelectric soft robot (TESR) system with the primary characteristics of power source from mechanical energy, is developed. At its heart is TESR with bioinspired architectures made of soft-deformable body and two triboelectric adhesion feet, which is driven and accurately controlled through triboelectric effect, while reaching maximum crawling speeds of 14.9 mm s⁻¹ on the acrylic surface. The characteristics of the TESR, including displacement and force, are tested and simulated under the power of a rotary freestanding triboelectric nanogenerator (RF-TENG). Crawling of TESR is successfully realized on different materials surfaces and different angle slopes under the driven of RF-TENG. Furthermore, a real-time visual monitoring platform, in which TESR carries a micro camera to transmit images in a long narrow tunnel, is also achieved successfully, indicating that it can be used for fast diagnosis in an area inaccessible to human beings in the future. This study offers a new insight into the sustainable power source technologies suitable for electrically responsive soft robots and contributes to expanding the applicability of TENGs.     

Computer-aided kidney segmentation on abdominal CT images.

In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation.     

CSM neural network for degraded printed character optical recognition

In pattern recognition applications, the classification power of a system can be improved by combining several classifiers. Obviously performance of the system cannot be improved if the individual classifiers make all the same mistakes, thus it is important to use different features and different structures in the individual classifiers. In this context, we propose a two subnets neural network called CSM net. The first subnet, or similarity layer, is operating as a similarity measure neural network; it is based on the complementary similarity measure method (CSM). The second subnet is a competitive neural network (CNN) based on the winner takes all algorithm (WTA) that is used for the classification. In the proposed neural architecture, the statistical CSM method is analyzed, and implemented in the form of a feed forward neural network, it is named “similarity measure neural network” (SMNN). We show that the resulting SMNN synaptic weights are modified versions of the model patterns used in the training set, and that they can be considered as a memory network. We introduce a relative distance data calculated from the SMNN output, and we use it as a quality measurement tool of the degraded characters, what makes the SMNN classifier very powerful, and very well-suited for features rejections. This relative distance is used by the SMNN and compared to a first rejection threshold to accept, or reject, the incoming characters. In order to guarantee a higher recognition and reliability rates for the cascaded method, the SMNN is combined with a second subnet based on the WTA for classification using a second specific rejection threshold. These two submits combination (CSM net) boost the performance of the SMNN classifier. This is resulting in a robust multiple classifiers that can be used for setting the entire rejection threshold. The experimental results that we introduce are related to the proposed method, but the tests are introduced with various impulse noise levels, as well as the tests with broken and manually corrupted characters, and characters with various levels of additive Gaussian noise. The experiments show the effective ability of the model to yield relevant and robust recognition on poor quality printed checks, and show that the CSM net outperforms the previous works, both in efficiency and accuracy.     

Software Simulation in GSM Environment and Hardware Implementation of Improved Multi—band Excitation Vocoder

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