Wearable damaged clothing fabric image recognition system based on image restoration algorithm

The sudden attack of the new crown virus in 2020 has brought an unprecedented impact on the real estate market economy and has completely disrupted people's work and life rhythm! With the rapid development of the Internet, the Internet has penetrated into all aspects of people's lives. As soon as e-commerce was introduced, it was loved by the majority of young people and brought tremendous changes to people's lives. Based on this, this paper studies a real estate virtual e-commerce model based on big data. In the study of this model, this paper combines the advantages of e-commerce and virtual communities to design a more effective virtual e-commerce model. The analysis of e-commerce and virtual communities shows that the virtual e-commerce model designed in this paper is a more effective model, and the real estate virtual e-commerce model based on big data technology can serve the real estate industry. Do a good job in the sales reform of the real estate industry.

Health, work, and contributing factors on life satisfaction: A study in Norway before and during the COVID-19 pandemic☆

Aesthetic education is the education of beauty, aesthetic feeling and aesthetic appreciation. Its task is to cultivate people's hearts with beauty, improve people's ability to appreciate and create beauty, and sow the seeds of beauty in people's hearts. Social media has flourished after deep integration with the global Internet. It has the distinctive characteristics of quickness, integration, personalization, interactivity and openness, and it is effectively integrated into people's study, life and work, and subtly changes people's life, thinking and habits. In the process of aesthetic education, on the one hand, educators educate people with beautiful objects, on the other hand, in the process of aesthetic education, the educated are in a state of aesthetic passion with strong emotional activities. This is very different from other education. Teenagers' aesthetic education should always adhere to the combination of theoretical research and practical experience, indoctrination education and inspiration education, explicit education and implicit education, and teacher guidance and personal perception as the core.

With the rapid development of Internet technologies such as big data and artificial intelligence, chat robots have begun to enter people's daily life, providing a lot of convenience for people's life and work. At present, many research institutions focus on the research and development of chat robots, but this field is still in its infancy, and chat robots in the market are more or less deficient. Based on the deep learning technology in big data, this paper constructs and trains the model of attention mechanism, and realizes the generative dialogue chat robot in the open field, which can intelligently generate smooth responses according to the wide input of users; The model is implemented by deep learning TensorFlow coding library, and the results show that the model proposed in this paper is better than the traditional language model in continuous and meaningful dialogue.

Flat icon design has been widely applied in web pages and mobile phone UI interface, and has been widely praised for its simplicity, clear color, and directness of information. With the rapid development of Internet e-commerce, according to the survey report of China Internet Network Information Center CNNIC (the 45th), until March 2020, the scale of China's mobile phone netizens has reached 897 million. Mobile phone has become an indispensable part of People's Daily life. It not only provides convenience for people's life and shopping, but also becomes a platform for people's work, study and education. The development of multimedia mobile phones has promoted the flat icon design to flourish. Through analysis and research in this paper, it is concluded that the UI interface flat icon has distinct visual characteristics: concise graphic design elements, strong visual impact color design, distinct order, and so on. According to the visual performance characteristics of the flat icon, a large number of cases are analyzed to obtain the style classification and specific design method of the flat icon design. Therefore, this paper hopes to provide references for relevant designers by analyzing and studying the features of flat icon and design method.

In this paper, Field Programmable Gate Array (FPGA) hardware structure is designed to accelerate the graph neural network model, and suitable hardware structure is designed for different computing modules to accelerate it, so that the function of the whole system can be improved. The performance of computing intensive applications can be improved by using heterogeneous systems, which are composed of various processor architectures, such as FPGA. Graph Neural Network (GNN) is a framework that uses deep learning to learn graph structure data directly in recent years. Its excellent performance has attracted scholars’ high attention and in-depth exploration. By formulating certain strategies on the nodes and edges of the graph, the graph neural network converts the graph structure data into a standard representation, and inputs it into a variety of different neural networks for training. Good results have been achieved in the task of node classification, edge information dissemination and graph clustering.

With the continuous development of The Times and technology, artificial intelligence algorithm and AI technology based on Field Programmable Gate Array (FPGA) technology have been widely used in people's life. This paper introduces the extensive application of artificial intelligence technology in production and life in recent years and its challenges. In view of the complexity of AI algorithm operation, real-time requirements and fast update and iteration characteristics, this paper proposes that FPGA chip can better solve these problems. This paper introduces the working principle of FPGA chip to realize digital processing and other hardware resources in detail, and analyzes the application advantages of FPGA in AI industry, life, and engineering. Compared with other chips, FPGA has the characteristics of low cost and flexible implementation of new algorithms, to achieve the balance between performance and cost. Finally, this paper summarizes the advantages of FPGA compared with other chips, and puts forward the possible development way and development platform in the future.

Defect inspection is a crucial part of industrial manufacturing. However, it relies heavily on human effort on manual visual inspection. Various machine vision techniques have been introduced to replace human labour and to improve inspection quality and efficiency. The limitation of these techniques is that the algorithms need to be engineered again with each different use case. In this work, a Convolutional Neural Network (CNN) is used to classify the defects of the Chemical Mechanical Planarization (CMP) ring. The trained CNN model achieved an accuracy of 91% and the time taken for each inference process is around 1800 msec. To achieve computational efficiency, the CNN model is performed on the embedded device. The previous implementation of embedded CNN deploys OpenCL-based high-level synthesis accelerator on a high-end Field Programmable Gate Array (FPGA) board. In this work, the model inference is accelerated by PipeCNN FPGA implementation on Cyclone-VSE DE1-SoC, a low-end embedded FPGA board. Several configurations of hardware parameters are tested to search for the best setup of the FPGA resources. The hardware implementation has improved approximately seven times faster, as the inference time for each classification has improved from 1800 msec to 250 msec. However, the model implemented using the hardware is observed to produce lower inference accuracy as the accuracy drops from 91% to 81%.

With the rapid development of Internet technology, computer network technology has been applied in all walks of life, which is closely related to people's work and life. In the information environment, people's daily life and production methods have also changed accordingly. The application of information technology in people's daily life further enriches people's lifestyle and promotes the rapid development of social economy. While the Internet provides convenience for users, it also brings certain information security risks to users. Therefore, in the application process of computer network information technology, it is necessary to increase information security precautions. Only in-depth implementation of computer network information security protection work, can effectively avoid the occurrence of information security problems. Therefore, it is necessary to ensure the security, effectiveness and integrity of network information through the implementation of relevant protection strategies. This paper analyzes the information security problems of computer network, and proposes corresponding protection strategies based on deep learning algorithm.

BackgroundParkinson disease (PD) impairs gait, balance, and quality of life, and wearable devices have been proposed to support rehabilitation, but evidence for their clinical efficacy remains uncertain.ObjectiveThis study aimed to evaluate, within the International Classification of Functioning, Disability, and Health (ICF) framework, the effects of wearable-device interventions on gait performance, balance, and health-related quality of life in people with PD by conducting a systematic review and meta-analysis of randomized controlled trials (RCTs).MethodsWe searched PubMed, Web of Science, Cochrane Library, Embase, and ClinicalTrials.gov from inception to November 18, 2025, for RCTs in people with PD comparing wearable-device interventions with control conditions. We used Hartung-Knapp random-effects models to pool mean differences (MDs) or standardized mean differences (SMDs) and reported 95% prediction intervals when ≥3 studies were pooled. Risk of bias was assessed using the Cochrane Risk of Bias (RoB) tool, and certainty of evidence was rated using Grading of Recommendations Assessment, Development, and Evaluation (GRADE).ResultsNine RCTs involving 260 participants were included. Wearable devices produced a small improvement in stride length (MD 0.10 meter, 95% CI 0.03-0.17), but there was no clear benefit for the 10-Meter Walk Test time (MD 0.04 second, 95% CI –0.06 to 0.15). Double support time showed no reduction (MD –1.59% gait cycle, 95% CI –3.79 to 0.61). Freezing of gait (Freezing of Gait Questionnaire [FOG-Q] and New Freezing of Gait Questionnaire [NFOG-Q]) did not significantly improve (SMD –0.24, 95% CI –0.72 to 0.24). Motor severity (Unified Parkinson Disease Rating Scale Part III [UPDRS III]) showed a small, nonsignificant trend favoring wearable devices (MD –2.16 points, 95% CI –4.39 to 0.07). For balance, pooled results from the Berg Balance Scale (BBS), Mini Balance Evaluation Systems Test (Mini-BESTest), and Performance-Oriented Mobility Assessment Balance Subscale (POMA balance) suggested a borderline effect (SMD 0.48, 95% CI –0.02 to 0.98). Wearable devices did not meaningfully improve Parkinson Disease Questionnaire (PDQ) scores (SMD –0.28, 95% CI –0.74 to 0.17), EQ-5D utility (MD 0.10, 95% CI –0.24 to 0.44), or Falls Efficacy Scale–International (FES-I) scores (MD –0.04, 95% CI –1.10 to 1.02). Prediction intervals frequently crossed the null, suggesting effects may vary by setting and population.ConclusionsWearable device interventions for Parkinson disease produced a small improvement on average in stride length, with no consistent benefits for other gait outcomes, balance, or patient-centered outcomes. By integrating ICF mapping with Hartung-Knapp meta-analysis, prediction intervals, and GRADE, and avoiding pooling of conceptually distinct gait measures used in prior reviews, this review clarifies where evidence is most consistent, supports using wearables as adjuncts to rehabilitation, and underscores the need for larger, longer RCTs with standardized outcomes to determine who benefits and how to implement them.Trial RegistrationPROSPERO CRD42024585686; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024585686

Nowadays, multimodal sentiment analysis has been integrated into people's lives. As an important research direction in the field of affective computing, by integrating and processing text, speech, and visual modal information, the accuracy of affective recognition has been greatly improved. This paper systematically sorts out the existing scientific research achievements and proposes a classification system based on modal combination, including technical strategies such as attention mechanisms and graph neural networks. In the classification of various modal combinations, the technical strategies and characteristics from early feature-level fusion to late model-level fusion and hybrid fusion methods are analyzed, with a focus on the research progress in directions such as cross-modal Transformer and modality-invariant representation learning. Then, by comparing the performance of each strategy on benchmark datasets such as IEMOCAP and CMU-MOSEI, the specific situations and specific application examples of the evolution of the integration method in terms of F1 value and resource consumption were analyzed. Further, the impact of modal misalignment and real-time constraints on performance was observed. And discuss emerging solutions such as self-supervised pre-training and conflict-aware loss functions. Ultimately, we look forward to future directions such as lightweight integration, edge device-oriented, and generalizable multimodal large language models.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery is guaranteed to converge, but it converges slowly. Its ordered-subset version (OS-EM) is used widely in tomographic image reconstruction because of its order-of-magnitude acceleration compared with the EM algorithm, but it does not guarantee convergence. Recently the ordered-subset, separable-paraboloidal-surrogate (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. We adapt the relaxed OS-SPS algorithm to the problem of image restoration. Because data acquisition in image restoration is different from that in tomography, we employ a different strategy for choosing subsets, using pixel locations rather than projection angles. Simulation results show that the relaxed OS-SPS algorithm can provide an order-of-magnitude acceleration over the EM algorithm for image restoration. This new algorithm now provides the speed and guaranteed convergence necessary for efficient image restoration.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery converges very slowly. Thus, the ordered subsets EM (OS-EM) algorithm has been widely used in image reconstruction for tomography due to an order-of-magnitude acceleration over the EM algorithm. However, OS-EM is not guaranteed to converge. The recently proposed ordered subsets, separable paraboloidal surrogates (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. In this paper, we develop a relaxed OS-SPS algorithm for image restoration. Because data acquisition is different in image restoration than in tomography, we adapt a different strategy for choosing subsets in image restoration which uses pixel location rather than projection angles. Simulation results show that the order-of-magnitude acceleration of the relaxed OS-SPS algorithm can be achieved in restoration. Thus the speed and the guarantee of the convergence of the OS algorithm is advantageous for image restoration as well.

Vision loss is a major challenge that affects people's lives in many aspects. In order to help people with visual impairment navigate and interact with those around them, wearable electronic devices and innovative technology can be used. The visually impaired system using Arduino and ultrasound uses advanced technology to convert ultrasound waves into useful information for visual people. Wearable devices, such as glasses or mobile devices can be used to receive audio signals and convert them into notifications to the user, via vibration, sounds or even text messages, which are equipped with ultrasonic sensors. When wearable devices emit high-frequency audio signals, these signals bounce off various obstacles in the environment around the person. The feedback signals are received and converted into visual information or alert messages for the user. People with visual impairment face great challenges because they depend heavily on others for their mobility. In this paper, a model was created to support blind people, facilitate their mobility, and ensure their safety through timely alerts of any obstacles in their path. The project includes a portable device that uses Arduino and is equipped with several sensors, such as distance and temperature sensors, to detect objects whose temperature exceeds 0 degrees Celsius. This device is designed to be hand-held with the glove and belt attached at the waist. Integrating all the mentioned sensors along with the Arduino that supervises the process.

At present, the development of science and technology in China is accelerating, and it has been deeply integrated with people's daily life and work. Taking computers as an example, it has become an important tool for people's work and entertainment. Based on the above content, this paper studies the application of image processing technology in Chinese calligraphy style feature extraction, with the aim of inheriting excellent traditional culture, analyzes the matters needing attention in the application of image processing technology, and summarizes relevant experience, hoping to provide a reasonable reference for workers in the same field.