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.

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.

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 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.

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.

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.

基于混沌和HVS的小波域自适应图像水印算法

针对常规的小波域水印算法自适应性差的缺陷,提出了一种新的基于混沌和人类视觉系统的小波域自适应图像水印算法。首先对载体图像进行二级DWT变换;然后对特定的中频子带进行分块,并对每块做SVD分解;最后将Logistic混沌置乱后的二值水印信息嵌入到每块的最大奇异值中,嵌入的强度根据HVS原理自适应地进行调整。仿真实验表明,该算法不仅具有很好的不可见性,且对JPEG压缩、加噪、滤波等常见的图像攻击具有很强的鲁棒性。

结合HVS和相似特征的图像质量评估方法

图像质量的客观评价方法研究在实现图像质量评价仪器化的过程中起到决定性的作用。在分析最新全参考图像质量评价算法:特征相似法(feature similarity,FSIM)的基础上,利用对比敏感度函数(contrast sensitivity function,CSF)算子以及离散余弦变换(discrete cosine transform,DCT)域的对比度掩盖效应,提出了一种改进的FSIM图像质量评价方法。该方法具有FSIM算法简单、高效等特性,同时又充分体现人眼视觉特性,更好地反映了人的主观感受。LIVE(laboratory for image and video engi-neering)测试数据集的实验结果证明,该方法在非线性回归后相关系数、斯皮尔曼相关系数、线外率等指标方面均优于传统的其他图像质量评价算法。

基于神经网络和HVS的空域数字水印算法

为了弥补空间域水印算法在提取水印时需要原宿主图像、鲁棒性较差等缺点,提出一种基于BP神经网络和人眼视觉特性(HVS)的空域数字水印算法。先选出原宿主图像对比度函数值最大的前块用于二值水印图像的嵌入,利用神经网络能够逼近任意非线性关系的特点和其自适应性,构建水印信号嵌入前后图像块像素值间的映射关系,实现了水印的盲提取,并将该算法与现有算法进行了对比。实验结果表明,对于JPEG压缩、加噪、剪切和旋转等常见的图像处理攻击,算法具有良好的鲁棒性和不可见性。

基于HVS的小波变换数字图像水印算法

为实现图像水印在数据认证、访问控制和版权保护等领域发挥作用,提出一种人眼视觉感知特性的小波变换数字图像水印算法。首先对二值图像水印进行Arnold变换,并采用行堆叠的方法,转换成一维序列,然后根据密钥生成的高斯序列中值的正负来确定水印信息嵌入时是采用那种调制方式,最后对原始图像进行3级DWT分解,在小波图像的第二层中频子带中,结合人眼视觉特性将水印正向或负向调制叠加在宿主图像大于JND值的小波系数上。实验结果表明:该算法能抵抗JPEG压缩、加噪、剪切等多种攻击,具有良好的透明性,鲁棒性和安全性。

HVS模型及其在静止图象压缩质量评价中的应用

图象质量尺度是最优化图象压缩算法参数和提高图象质量的重要依据 .建立在人类视觉模型 (HVS)基础之上的感知质量尺度作为主客观联系的桥梁 ,能有效地反映出人对图象失真在视觉上的感知 .近年来 ,有许多研究者借助人类视觉系统研究中的最新成果 ,深入分析了与图象质量密切相关的视觉感知特性 ,提出了大量效果不错的静止图象压缩感知质量尺度 ,并对视觉感知特性在图象质量尺度中的应用方法进行了较全面的综述 ,揭示了影响其图象质量预测准确性、鲁棒性的主要因素 ,给出了该领域的最新研究成果和未来发展方向 .

一种HVS和Contourlet结合的图像水印算法

提出了一种Contourlet域的图像水印算法.首先对图像进行多层分解;然后利用视觉系统的亮度纹理特性,把伪随机序列组成的水印信号自适应地嵌入到能量最大的子带中;最后经过逆变换得到嵌入水印后的图像.该算法解决了直接嵌入边缘扭曲的问题,检测端使用基于相关的盲检测.实验结果表明,该算法能够在满足不可见性的条件下抵抗JPEG压缩、加噪和剪裁等攻击,具有很强的鲁棒性.

基于HVS的小波图像融合新算法

目前的大多数基于小波变换的图像融合算法 ,由于并没有细致考虑人眼的视觉特性 ,因而得到的融合效果有时并不理想。为了提高图像的融合效果 ,提出了一种新的基于人类视觉系统 (HVS)的小波图像融合新算法。该算法充分考虑了人类视觉系统的特点 ,首先在多尺度小波分解的基础上 ,计算出每层每个像素邻域的纹理和边缘亮度相关量 ;然后自适应地求得加权融合系数 ;最后采用多层次迭代法来产生融合图像。实验结果表明 ,该算法不仅能使图像融合结果有较大的改善 ,而且有利于人眼对目标的探测和识别。

基于多分辨率和HVS的盲图像水印隐藏方法

文章提出了一种盲图像水印隐藏方法，结合了人类视觉系统（ ＨＶＳ）和图像的 ＤＷＴ多分辨分解，实验结果表明这个方法是有效的。