Intelligent 3D garment system of the human body based on deep spiking neural network

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

Exploring the Association between Climate Change and Human Development: A Visual Analytics Study

This study explores the complex relationship between climate change and human development. The aim is to understand how climate change affects human development across countries, regions, and the global population. Visual analytics were used to examine the impact of various climate change indicators on different aspects of human development. The study highlights the urgent need for climate change action and encourages policymakers to make decisive moves. Climate change adversely affects numerous aspects of daily life, leading to significant consequences that must be addressed through policy changes and global governance recommendations. Key findings include that regions with higher CO2 emissions experience a significantly higher incidence of life-threatening diseases compared to regions with lower emissions. Additionally, higher CO2 emissions correlate with consistent death rates. Increased pollution exposure is associated with a higher prevalence of life-threatening diseases and higher rates of malnutrition. Moreover, greater mineral depletion is linked to more frequent life-threatening diseases, suggesting that industrialization contributes to adverse health effects. These results provide valuable insights for policy and decision-making aimed at mitigating the impact of climate change on human development.

Human Visual Attention Mechanism-Inspired Point-and-Line Stereo Visual Odometry for Environments with Uneven Distributed Features

Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.

SAR Image Quality Assessment System Based on Human Visual Perception for Aircraft Electromagnetic Countermeasures

In electronic confrontation, Synthetic Aperture Radar (SAR) is vulnerable to different types of electronic jamming. The research on SAR jamming image quality assessment can provide the prerequisite for SAR jamming and anti-jamming technology, which is an urgent problem that researchers need to solve. Traditional SAR image quality assessment metrics analyze statistical error between the reference image and the jamming image only in the pixel domain; therefore, they cannot reflect the visual perceptual property of SAR jamming images effectively. In this demo, we develop a SAR image quality assessment system based on human visual perception for the application of aircraft electromagnetic countermeasures simulation platform.The internet of things and cloud computing techniques of big data are applied to our system. In the demonstration, we will present the assessment result interface of the SAR image quality assessment system.

Evaluating Methods of Visual Assistance for Workers to Improve Quality and Usability in Individualized Kitchen Cabinet Assembly

In times of digitalisation, visual assistance systems in assembly are increasingly important. The design of these assembly systems needs to be highly complex to meet the requirements. Due to the increasing number of variants in production processes, as well as shorter innovation and product life cycles, assistance systems should improve quality and reduce complexity of assembly processes. However, many large kitchen manufacturers still assemble kitchen cabinets manually, due to the high variety of components, such as rails and fittings. This paper focuses on the analysis and evaluation of virtual assistance systems to improve quality and usability in individualised kitchen cabinet assembly processes at a large German manufacturer. A solution is identified and detailed.

Magnetic resonance diffusion tensor imaging and diffusion tensor tractography of human visual pathway

AIM: To investigate the visual pathway in normal subjects and patients with lesion involved by diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT). METHODS: Thirty normal volunteers, 3 subjects with orbital tumors involved the optic nerve (ON) and 33 subjects with occipital lobe tumors involved the optic radiation (OR) (10 gliomas, 6 meningiomas and 17 cerebral metastases) undertook routine cranium magnetic resonance imaging (MRI), DTI and DTT. Visual pathway fibers were analyzed by DTI and DTT images. Test fractional anisotropy (FA) and mean diffusivity (MD) values in different part of the visual pathway. RESULTS: The whole visual pathway but optic chiasm manifested as hyperintensity in FA maps and homogenous green signal in the direction encoded color maps. The optic chiasm did not display clearly. There was no significant difference between the bilateral FA values and MD values of normal visual pathway but optic chiasm, which the FA values tested were much too low (all P>0.05). The ONs of subjects with orbital tumors were compressed and displaced. Only one subject had lower FA values and higher MD values. OR of 9 gliomas subjects were infiltrated, with displacement in 2 and disruption in 7 subjects. All OR in 6 meniongiomas subjects were displaced. OR in 17 cerebral metastases subjects all developed displacement while 7 of them had disruption also. CONCLUSION: MR-DTI is highly sensitive in manifesting visual pathway. Visual pathway can be analyzed quantitatively in FA and MD values. DTT supplies accurate three dimensional conformations of visual pathway. But optic chiasm's manifestation still needs to improve.

Human melanopsin-AAV2/8 transfection to retina transiently restores visual function in rd1 mice

AIM： To explore whether ectopic expression of human melanopsin can effectively and safely restore visual function in rd1 mice.· METHODS： Hematoxylin-eosin staining of retinal sections from rd1 mice was used to detect the thickness of the outer nuclear layer to determine the timing of surgery. We constructed a human melanopsinAAV2/8 viral vector and injected it into the subretinal space of rd1 mice. The Phoenix Micron IV system was used to exclude the aborted injections, and immunohistochemistry was used to validate the ectopic expression of human melanopsin. Furthermore, visual electrophysiology and behavioral tests were used to detect visual function 30 and 45 d after the injection. The structure of the retina was compared between the human melanopsin-injected group and phosphate buffer saline（PBS）-injected group.·RESULTS： Retinas of rd1 mice lost almost all of their photoreceptors on postnatal day 28（P28）. We therefore injected the human melanopsin-adeno-associated virus（AAV） 2/8 viral vector into P30 rd1 mice. After excluding aborted injections, we used immunohistochemistry of the whole mount retina to confirm the ectopic expression of human melanopsin by co-expression of human melanopsin and YFP that was carried by a viral vector. At30 d post-injection, visual electrophysiology and the behavioral test significantly improved. However,restoration of vision disappeared 45 d after human melanopsin injection. Notably, human melanopsin-injected mice did not show any structural differences in their retinas compared with PBS-injected mice.·CONCLUSION： Ectopic expression of human melanopsin effectively and safely restores visual function in rd1

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

New algorithm for infrared small target image enhancement based on wavelet transform and human visual properties

The key to the wavelet based denoising teehniquea is how to manipulate the wavelet coefficients. By referring to the idea of Inclusive-OR in the design of circuits, this paper proposes a new algorithm called wavelet domain Inclusive-OR denoising algorithm（WDIDA）, which distinguishes the wavelet coefficients belonging to image or noise by considering their phases and modulus maxima simultaneously. Using this new algorithm, the denoising effects are improved and the computation time is reduced. Furthermore, in order to enhance the edges of the image but not magnify noise, a contrast nonlinear enhancing algorithm is presented according to human visual properties. Compared with traditional enhancing algorithms, the algorithm that we proposed has a better noise reducing performanee , preserving edges and improving the visual quality of images.

A ROBUST ADAPTIVE VIDEO ENCODER BASED ON HUMAN VISUAL MODEL

A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed. The encoder combines the best features of Fine Granularity Scalable (FGS) coding, framedropping coding, video redundancy coding, and human visual model. According to packet loss and available bandwidth of the network, the encoder adjust the output bit rate by jointly adapting quantization step-size instructed by human visual model, rate shaping, and periodically inserting key frame. The proposed encoder is implemented based on MPEG-4 encoder and is compared with the case of a conventional FGS algorithm. It is shown that RAVE is a very efficient robust video encoder that provides improved visual quality for the receiver and consumes equal or less network resource. Results are confirmed by subjective tests and simulation tests.

Human Visual Acuity in Analysis of Intervention in the Landscape of Coxilha Rica/Brazil

The Drovers’ Paths are remnants of important land access roads from Rio Grande do Sul to São Paulo at the time of Colonial Brazil. They were built and used between the 18th and 20th centuries, particularly in the region of Coxilha Rica. The main objective of this research is to develop a method for decision-making applied to the territorial landscape management in the Coxilha Rica. The method consisted of generating criteria to map the visibility spot reached from the main selected points;define the human visual acuity, realize bibliographic research, use cartographic and historical documents, inter-views, as well as field surveys that enabled the identification, characterization and mapping of historical farms and drovers’ paths. After data processing, the information was entered into the cartographic database;the data were cross-checked and analysis was made of the visibility of the surrounding farms and stone-walled corridors. Quality assessments showed that, with the visibility polygons, and through the use of cartographic tools, we could cross-check between different levels of information and analyze landscape intervention alternatives in order to minimize environmental impacts. When applying the method in the Coxilha Rica it was possible mapping the visibility polygon, taking human visual acuity into consideration, based on historical farms and stone-walled corridors;and making spatial analyses to explore alternatives to intervention (installation of power transmission systems) in order to preserve the scenic environment of the region. In the end, the decision was by does not construct the system.

Rendering algorithms for aberrated human vision simulation

Vision-simulated imagery―the process of generating images that mimic the human visual system―is a valuable tool with a wide spectrum of possible applications, including visual acuity measurements, personalized planning of corrective lenses and surgeries, vision-correcting displays, vision-related hardware development, and extended reality discomfort reduction. A critical property of human vision is that it is imperfect because of the highly influential wavefront aberrations that vary from person to person. This study provides an overview of the existing computational image generation techniques that properly simulate human vision in the presence of wavefront aberrations. These algorithms typically apply ray tracing with a detailed description of the simulated eye or utilize the point-spread func-tion of the eye to perform convolution on the input image. Based on the description of the vision simulation tech-niques, several of their characteristic features have been evaluated and some potential application areas and research directions have been outlined.

On‐device audio‐visual multi‐person wake word spotting

Audio‐visual wake word spotting is a challenging multi‐modal task that exploits visual information of lip motion patterns to supplement acoustic speech to improve overall detection performance.However,most audio‐visual wake word spotting models are only suitable for simple single‐speaker scenarios and require high computational complexity.Further development is hindered by complex multi‐person scenarios and computational limitations in mobile environments.In this paper,a novel audio‐visual model is proposed for on‐device multi‐person wake word spotting.Firstly,an attention‐based audio‐visual voice activity detection module is presented,which generates an attention score matrix of audio and visual representations to derive active speaker representation.Secondly,the knowledge distillation method is introduced to transfer knowledge from the large model to the on‐device model to control the size of our model.Moreover,a new audio‐visual dataset,PKU‐KWS,is collected for sentence‐level multi‐person wake word spotting.Experimental results on the PKU‐KWS dataset show that this approach outperforms the previous state‐of‐the‐art methods.

数字人文视域下馆藏清代土尔扈特档案文献可视化开发

清乾隆年间,土尔扈特部回归祖国事件形成的档案文献是中华民族共同体演进发展的档案佐证。数字人文环境下,对其档案进行可视化开发,可深入挖掘档案文本中的民族交往交流交融价值,提升档案馆知识服务效能,满足多元利用需求。据此,在阐释土尔扈特档案文献内涵外延、资源特征及其可视化开发理论与价值的基础上,提出以项目实施为依托的“多元共建共享”土尔扈特档案文献可视化开发方案。从档案的数据化整合、语义化处理与可视化服务等流程建构可视化开发路径,对于发掘土尔扈特档案文献中蕴含的爱国主义精神、维护新疆民族地区稳定发展,铸牢中华民族共同体意识具有重要现实意义。

空间科学实验数据可视化分析系统技术研究

为提升空间科学实验数据的分析效率与利用水平,设计以数据可视化为切入点,支持多领域、长周期空间科学实验数据交互分析的系统架构,对系统关键技术进行研究。设计分析算法动态扩展和服务映射流程,支持数据分析算法的扩展与更新,建立算法的快速处理模式和复杂分析模式,兼容与支持不同类型分析算法的处理流程。通过多可视化组件之间的数据联动,直观且同步呈现相关联数据之间的相关性。采用可视化视图交互调整和布局,支持不同数据、不同显示屏幕的个性可视化需求。系统应用表明:系统已集成了十余种空间科学实验数据分析算法,实现了多领域空间科学实验数据的可视化分析。

医学人文教育教学改革课题多维度可视化分析研究

本文采用统计、分词、可视化等分析工具,从课题负责人、研究主题、研究趋势等多个维度,对首都医科大学医学人文学院近5年获批的教改课题进行分析,结合医学人文教改实践成效,探讨了新时代教育新理念和新要求对医学人文教育教学改革的促进,医学人文教育平台提供多学科交叉融合,教育教学改革实践促进教师教学能力发展,为推进医学人文教育教学改革创新提供了思路。

基于贪婪算法的云计算数据块节能存储仿真

针对云数据储存能量消耗大的问题,提出基于贪婪算法的云计算数据块节能存储方法。建立具有用户访问层、核心服务层和服务管理层的云计算架构,了解数据块产生过程和储存环境;将物理机利用率、能源消耗量和主机储存能力作为节能储存的约束条件,将待储存的数据块封装为虚拟机,利用贪婪算法描述虚拟机部署问题,构建贪婪算法下虚拟机分配环境;计算单个物理机和整个数据中心的数据块储存能力和资源请求能力,综合考虑虚拟机分配的相关向量,运算数据储存时的能量消耗;以总体能量最小为目标函数,将虚拟机分为主模块与备用模块,通过设置虚拟机状态转换规则来减少储存开销,实现节能储存。实验结果表明,上述方法在数据储存过程中能够有效减少服务器开启数量,节省储存功率,达到节能目的。

多矢量姿态测量算法研究综述

将矢量测量转化为姿态测量,该问题在姿态估计中被称为Wahba问题。介绍了最小化Wahba问题的损失函数的不同算法,包括最优四元数估计器(ESOQ)、四元数估计器(QUEST)、快速最优姿态矩阵(FOAM)、矩阵的奇异值分解(SVD)等广泛使用的算法,以及基于快速线性四元数姿态估计器(FLAE)、黎曼流形等近年来提出的算法。给出了简单的计算原理与推导过程,并通过计算机仿真对照,归纳各类算法在计算旋转矩阵时的计算精度、鲁棒性。针对姿态测量算法应对当前应用场景的性能需求,简要介绍其在惯性测量单元(IMU)动态对准、协同集群视觉定姿、图像拼接等方面的适用可能和工作原理,并在此基础上简述了姿态测量算法当前的缺陷与未来的发展趋势。

AI+BCI硅基碳基融合新智能的开始

我们正迎来人类发展的第四次浪潮,正处于从信息社会向人类社会-物理世界-信息空间融合的智能社会的关键转型期。近年来,计算和信息技术飞速发展,深度学习的空前普及和成功将人工智能(AI)确立为人类探索机器智能的前沿领域。与此同时,得益于器件的革命性进展和人工智能(AI)的发展,脑机接口(BCI)植入技术同样快速落地,这意味着BCI+AI碳基硅基融合的开始,然而,硅基和碳基运算的底层逻辑存在根本差异,脑的智能机制仍有待进一步探索。本研究提出的视觉认知引导的孪生AI深度网络,是由个人意识驱动的深度网络技术,通过捕捉并解析个体的思维模式和创意灵感,为每个用户量身打造独特的视觉世界。在这样的环境中,每个人都成为自己创造世界的视觉主导者,打破物质和意识的壁垒,得以展现丰富的个性和创造力。