Information Acquisition Research Articles

Enhanced TomoSAR imaging method with complex least squares for 3D building reconstruction

ABSTRACT Synthetic aperture radar tomography (TomoSAR) has emerged as an advanced technology for the rapid acquisition of three-dimensional information, with model solutions dependent on a specific complex least squares adjustment criterion. We design two TomoSAR imaging algorithms based on two adjustment criterions minimizing the square sum of the real and imaginary components of observation vector residual, and minimizing the square sum of the L2-norm of observation vector residual. Additionally, we propose a strong scatterer recognition method that combines amplitude thresholding and density-based spatial clustering of applications with noise to alleviate the low efficiency and excessive noise in real images. The simulated and practical experiments are conducted to evaluate the performance of algorithms. Results suggested that two algorithms perform similarly in parameter estimation accuracy and noise immunity for buildings with simple structures and single scatterer. However, for buildings with multiple scatterers, the algorithm derived by minimizing the square sum of the L2-norm from the observation vector residual exhibits more robustly than the other algorithm, accurately distinguishing multiple scatterers within pixels and reconstructing 3D scenes mirroring the actual ones. The results verify the effectiveness of the proposed strong scatterer recognition method and the importance of the adjustment criterion in TomoSAR imaging.

Signal Processing for Novel Noise Radar Based on de-chirp and Delay Matching

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments.

Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets

Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. Conventional pose estimation for space targets unfolds in two stages: establishing 2D–3D correspondences using keypoint detection networks and 3D models, followed by pose estimation via the perspective-n-point algorithm. The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. To tackle the aforementioned challenges, we propose an adaptive dual-stream aggregation network (ADSAN), which enables the learning of finer local representations and the acquisition of abundant spatial and semantic information by merging features from both inter-layer and intra-layer perspectives through a multi-grained approach, consolidating features within individual layers and amplifying the interaction of distinct resolution features between layers. Furthermore, our ADSAN implements the selective keypoint focus module (SKFM) algorithm to alleviate problems caused by partial occlusions and viewpoint alterations. This mechanism places greater emphasis on the most challenging keypoints, ensuring the network prioritizes and optimizes its learning around these critical points. Benefiting from the finer and more robust information of space objects extracted by the ADSAN and SKFM, our method surpasses the SOTA method PoET (5.8°, 8.1°/0.0351%, 0.0744%) by 0.5°, 0.9°, and 0.0084%, 0.0354%, achieving 5.3°, 7.2° in rotation angle errors and 0.0267%, 0.0390% in normalized translation errors on the Speed and SwissCube datasets, respectively.

Impact of firm’s supply chain network position on digital transformation: evidence from China

Purpose Given the complexity and increasing interdependence of supply chain networks, a broader perspective beyond a simple binary relationship is needed to analyse the impact of supply chain networks on firms. Based on social network theory, this study aims to explore the relationship between firms’ network position in the supply chain network and digital transformation, as well as the moderating effects of structural holes and network partners’ digitalisation. Design/methodology/approach This study analyses a sample of Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges for the period 2011–2020 using regression analysis to test hypothesised relationships. Findings A firm’s centrality in the supply chain network is positively related to its digital transformation, and that the firm’s structural holes and the level of network partners’ digitalisation can both strengthen the positive influence of network centrality on digital transformation. Moreover, the heterogeneity analysis reveals that the positive effect of firms’ network centrality on digital transformation is more pronounced in non-SOEs and upstream firms. Practical implications In the pursuit of digital transformation, firms should recognise the significance of their position in the supply chain network and enhance their capability for information and resource acquisition by proactively strengthening their business cooperation with other network members. Moreover, firms should emphasise the value of open networks and their partners in supply chain networks. Originality/value This study applies social network theory to investigate the role of a firm’s external supply chain network in its digital transformation, clarifying the context and boundaries in which a firm’s position in the supply chain network influences digital transformation. The results extend the research and theoretical perspective on digital transformation and the consequences of supply chain networks.

Effects and Mechanisms of TikTok on Self-Rated Health of Older Adults in China During the COVID-19 Pandemic: A Mediation Analysis

Background/Objectives: During the COVID-19 pandemic, TikTok emerged as a significant app for entertainment and information acquisition for the general public, gradually extending its reach to the older population. Digital technology, exemplified by TikTok, has become an indispensable part of older adults’ lives. However, while prior studies have extensively focused on the impact of internet use on older adults’ health, research on the impact of TikTok during the COVID-19 pandemic remains insufficient. Methods: Utilizing data from the China Family Panel Studies (CFPS) 2020, this study explored the impact of TikTok use on older adults’ self-rated health and its underlying mechanisms through multiple regression and mediation analysis. Results: The study found that (1) TikTok use had a significant positive effect on the self-rated health of older adults; (2) while TikTok use increased the exercise frequency of older adults, exercise frequency did not affect their self-rated health; and (3) TikTok use boosted older adults’ protein intake, contributing substantially to their self-rated health, accounting for 38.7% of the total effect. Conclusions: It is advisable to actively promote the popularity of short video apps such as TikTok among older adults, as they serve as an effective tool for disseminating health information and encouraging healthier lifestyles and behaviors.

Estimating coffee crop parameters through multispectral imaging and machine learning algorithms

Abstract. Brazil plays a crucial role in the global economy due to its significant contribution to the agricultural sector, particularly in coffee production, where it stands out as the largest producer and exporter of processed coffee. Various disturbances can influence coffee plants, causing abnormalities that can hinder their successful growth. Parameters such as plant height and canopy diameter play an essential role in assessing the health and productivity of the plants, reflecting their growth, development, and ability to capture sunlight. Additionally, height is also related to the balanced distribution of nutrients and water, providing valuable information about overall performance and the capacity for healthy production. In this regard, the application of methodologies involving remote sensing and machine learning algorithms has shown promising results in the rapid and safe acquisition of information about agricultural systems. This study evaluates different machine learning algorithms, using radiometric values from multispectral images obtained by remote sensing platforms as input datasets for estimating plant height and canopy diameter in coffee cultivation. The best performance was observed for architectures that showed lower RMSE and RMSE% values. For the plant height parameter (m), the RGB sensor exhibited the best performance using the Random Tree algorithm, with an RMSE (0.27) and RMSE% (8.80). For the canopy diameter (m), the sensor showed the best performance using the Random Forest algorithm, with an RMSE (0.15) and RMSE% (8.16).

Channel reconstruction through improvised deep learning architecture for high-speed networks

Efficient acquisition of channel state information (CSI) is quite complicated process but immensely essential to exploit probable benefits of massive multiple input multiple output (MIMO) systems. Therefore, a deep learningbased model is utilized to estimate channel feedback in a massive MIMO system. The proposed improvised deep learning-based channel estimation (IDLCE) model enhances channel reconstruction efficiency by using multiple convolutional layers and residual blocks. The proposed IDLCE model utilizes encoder network to compress CSI matrices where decoder network is used to downlink reconstruct CSI matrices. Here, an additional quantization block is incorporated to improve feedback reconstruction accuracy by reducing channel errors. A COST 2,100 model is adopted to analyse performance efficiency for both indoor and outdoor scenarios. Further, deep learning-based model is used to train thousands of parameter and correlation coefficients much faster and to minimize computational complexity. The proposed IDLCE model evaluate performance in terms of normalized mean square error (NMSE), correlation efficiency and reconstruction accuracy and compared against varied state-of-art-channel estimation techniques. Excellent performance results are obtained with large improvement in channel reconstruction accuracy

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Recovering the Anchoring of Economic Valuations

We revisit the interpretation of the anchoring effect as evidence for nonexisting or arbitrary preferences. A theory of endogenous information acquisition (i.e., deliberation) is developed to rationalize the causal dependence of economic valuations on arbitrary anchor numbers. We identify theory-driven moderators to reconcile seemingly discrepant findings among original and follow-up anchoring experiments. We demonstrate in a meta-analysis that the anchoring effect may be systematically moderated by unintended experimental/individual differences. Moreover, apparent replication failure of the anchoring effect may be a false negative for self-countervailing treatment effects, because an anchoring treatment may cast opposite effects on different subjects within an experiment. (JEL C90, D11, D83)

Reinforcement learning and rational expectations equilibrium in limit order markets

This paper shows that simple payoff-based reinforcement learning can help to achieve rational expectations equilibrium in limit order markets. In equilibrium, speculators mainly supply liquidity, while liquidity consumption increases in the private values of no-speculators with intrinsic motives for trade. Driven by information acquisition of the non-speculators, liquidity consumption is hump-shaped in fundamental volatility for the speculators but U-shaped for the non-speculators. In contrast, liquidity supply decreases in fundamental volatility for the speculators but is hump-shaped for the non-speculators. Unlike the informed traders who trade on asset fundamentals, the uninformed traders trade more on order book and trading information.

Second-price auctions with information acquisition costs

Second-price auctions with information acquisition costs

대학생의 디지털 리터러시 변화에 대한 종단연구: 잠재성장모형을 적용하여

The purpose of this study was to examine the change patten of college students’ digital literacy by using the data of ‘Korean Media Panel Data for the 12th(2021), 13the(2021), 14th(2023) year conducted by Korea Information Society Development Institute(KISDI). Four sub-competencies of information utilization, information acquisition, media utilization, and personal information recognition were selected as digital literacy competencies. The latent growth model was utilized to analyze longitudinal change with influence of independent variables. The major findings of this research were as followings. First, college student’s digital literacy showed linearly increased. Second, it was confirmed that the major(engineering sciences), SNS(social network service) and OTT(over-the-top media service) consumption behavior had a positive effect on the initial level and change in digital literacy, while gender had no significant differences in digital literacy. Third, the predictors(major, SNS and OTT consumption) had significant effects on information utilization, information acquisition. Based on the results of study, we suggest that it is necessary to care of non-science and Engineering Students. Also, it is necessary to provide opportunities and programs for individual student to use digital platforms and online contents.

MIMO-Uformer: A Transformer-Based Image Deblurring Network for Vehicle Surveillance Scenarios

Motion blur is a common problem in the field of surveillance scenarios, and it obstructs the acquisition of valuable information. Thanks to the success of deep learning, a sequence of CNN-based architecture has been designed for image deblurring and has made great progress. As another type of neural network, transformers have exhibited powerful deep representation learning and impressive performance based on high-level vision tasks. Transformer-based networks leverage self-attention to capture the long-range dependencies in the data, yet the computational complexity is quadratic to the spatial resolution, which makes transformers infeasible for the restoration of high-resolution images. In this article, we propose an efficient transformer-based deblurring network, named MIMO-Uformer, for vehicle-surveillance scenarios. The distinct feature of the MIMO-Uformer is that the basic-window-based multi-head self-attention (W-MSA) of the Swin transformer is employed to reduce the computational complexity and then incorporated into a multi-input and multi-output U-shaped network (MIMO-UNet). The performance can benefit from the operation of multi-scale images by MIMO-UNet. However, most deblurring networks are designed for global blur, while local blur is more common under vehicle-surveillance scenarios since the motion blur is primarily caused by local moving vehicles. Based on this observation, we further propose an Intersection over Patch (IoP) factor and a supervised morphological loss to improve the performance based on local blur. Extensive experiments on a public and a self-established dataset are carried out to verify the effectiveness. As a result, the deblurring behavior based on PSNR is improved at least 0.21 dB based on GOPRO and 0.74 dB based on the self-established datasets compared to the existing benchmarks.

영유아기 자녀를 둔 아버지의 온라인(on-line) 양육참여 경험의 의미와 개선요구

Objectives This study aimed to explore the meaning and the demand of online involvement in parenting for fathers raising young children. Methods To this end, a qualitative study centered on depth-interview with 8 fathers raising young children was conducted to specifically analyze the actual experiences of fathers in online involvement in parenting. Results First, it was analyzed that fathers' on-line involvement in parenting is meaningful as an effective channel for getting closer to their children through online and offline mixed play, personalized education through online-based customized information acquisition, further activating child-rearing-related communication, and achieving child-rearing goals and solving problems. Second, it was found that fathers required reliable standards for online parenting and educational information, family-friendly online content and platform development and dissemination, parental education support for content selection, and hardware support at the national and regional level to improve on-line involvement in parenting. Conclusions The main context of online involvement in parenting for fathers raising young children was the expansion of play between fathers and children through online and offline connection, and in this process, it was found that social and emotional meaning was formed to form a closer relationship with each other. In addition, it was found that fathers' online-based involvement in parenting enabled information tailored to their children's personal characteristics, enabling individualized education for children, and further activating communication with spouses and families and children.

The Infringement of Deepfake Technology on Personal Privacy and Legal Protection: A Discussion Based on Article 1032 of the Civil Code

The rapid development of deepfake technology has infringed upon personal privacy in the form of the disclosure and misuse of personal information, infringement upon the peace of people's life, and damage to personal image and reputation. The disclosure and misuse of personal information should be analysed from the perspectives of personal use and commercial use of information data; infringement upon the peace of people's life should be examined from the perspectives of fraud and harassment, and malicious pursuit using deepfake technology; and damage to personal image and reputation should be discussed from the two levels of initial damage to personal image and reputation and aggravated damage to personal reputation after dissemination through the media. Article 1032 of the Civil Code clearly defines the scope of privacy protection as the peace of people's private life, private space, private activities and private information, as well as the infringement of privacy rights through means such as spying, intrusion, disclosure and publication, which reflects the legislative inertia to strengthen the protection of privacy and personal information. Article 1032 of the Civil Code, the Personal Information Protection Law, the Provisions on the Administration of Deep Synthesis of Internet-based Information Services, and other laws and regulations together form the current personal privacy protection system. However, existing laws do not provide specific explanations for key terms such as 'leakage', 'intrusion' and 'damage', which leads to uncertainty in the application of the law and makes it difficult to effectively protect personal privacy in actual operations. At the same time, there is a lack of clear definitions of the scenarios and specific standards of behaviour that the technology may involve, which has exacerbated public concerns about personal privacy security. Strengthening legal protection against deepfake technology requires refining legal concepts and drawing on knowledge from comparative law: 'leakage' can be considered the illegal acquisition and dissemination of unauthorised data and information, 'intrusion' can be defined as behaviour that disrupts the normal order of others and causes mental distress, and 'damage' can be understood as behaviour that causes negative evaluation or loss to the image, reputation or property of others. The legislative department should take the lead and cooperate with the Ministry of Industry and Information Technology in formulating specific standards for evaluating behaviour. The deterrent effect of the law should be strengthened by refining relevant legal provisions in the Civil Code and the Personal Information Protection Law and increasing the penalties for violations of the law. This will better balance technological development and personal privacy protection and improve China's personal privacy protection system.

Design and Application of Real‐Time and High‐Precision Seismic Acquisition Station in Antarctic Region

ABSTRACTIn this study, a polar seismic acquisition station based on the FPGA + ARM architecture was successfully developed. This system can achieve real‐time multifunctional acquisition and transmission of seismic signals and environmental information in the extreme low‐temperature environment of Antarctica at −40°C. The system adopts a modular design, with high‐precision data acquisition encoded by the main controller and uploaded to the host computer in real‐time. Integrated display controls enable real‐time data display and acquisition station location mapping for user interaction. We propose improving data transmission in traditional seismic acquisition stations by adopting a distributed computing model with data segment return. Integrating the first arrival picking algorithm as an IP core will reduce computational demands on the data processing center. This acquisition system features high precision (24‐bit), a wide dynamic range (> 130dB), low noise (0.5 V rms@40dB), and high‐precision timing control (200 ns). It supports real‐time wireless data transmission over 120 channels up to 1 km. The system also implements a self‐storage plus wired and wireless networking solution, allowing flexible networking based on actual needs. Test results indicate that the ice sheet thickness at the acquisition site is approximately 526.49 m, validating the experimental design's effectiveness and providing technical support for subsequent drilling and seismic exploration.

Research on Phase Stabilization Algorithm of Femtosecond Timing System

This paper presents the design, implementation, and validation of a femtosecond timing system aimed at achieving precise time control and phase synchronization for large particle accelerators. A prototype system utilizing a continuous wave laser was developed, focusing on minimizing timing jitter and long-term phase drift. Key components include an optical delay line for coarse adjustments and a fiber stretcher for fine-tuning, achieving an adjustment precision of 1 femtosecond. The system incorporates a phase detection module with a non-In-phase/Quadrature downconversion approach, enabling high-accuracy phase measurements. A collaborative algorithm was designed to optimize the interplay between the optical delay line and the fiber stretcher, utilizing a proportional-integral-derivative (PID) control algorithm to enhance adjustment precision. A Field Programmable Gate Array (FPGA) served as the core interface converter, facilitating data communication and real-time phase information acquisition. Experimental results demonstrated significant improvements in phase stability, with average phase deviation reduced from 1374.104 fs to 15.782 fs, showcasing the effectiveness of the proposed system in achieving high precision and stability in phase control. This research provides a solid foundation for future advancements in timing systems for high-frequency reference signals.

A hybrid approach to investigating major management factors for effective highway preventive maintenance

To resolve the problem of the low management capacity of highway preventive maintenance (HPM), this paper identified and evaluated the major HPM management factors to improve management effectiveness and achieve sustainable highway development. The study conducted a literature review and exploratory factor analysis (EFA) to identify the major HPM management factors. Social network analysis (SNA) was used to distinguish the degree of importance of these factors. A system dynamics (SD) model was developed to explore their patterns of influence. The research identified six dimensions of HPM management, including the management system, management resources, management cognition, management decisions, management technology, and external conditions, along with 26 major management factors. Moreover, information acquisition, system perfection, etc., are key factors; system execution, manager capability, etc., are hub factors; and route selection, machinery allocation, etc., are non-key factors. These factors have a positive impact on HPM management, leading to an upward trend in management effectiveness. The main innovation provided a hybrid and comprehensive approach to identify and evaluate the major management factors for effective HPM. This study can guide managers in developing effective HPM plans, allocating resources more efficiently, improving the overall quality of highway maintenance and forming a sustainable transportation system.

All‐Optical Imaging Using Perovskite Nanocrystals Based on Spectro‐Spatial Correlation

AbstractThe exponential growth of data from sensors and internet‐connected devices has challenged traditional electronic computing, particularly in processing speed and power efficiency. Optical information processing, with its inherent high speed and parallelism, offers a promising solution. Moreover, optical processing is vital for developing all‐optical networks, where all‐optical imaging is essential. In this paper, a novel all‐optical imaging technique that leverages in situ anion exchange reactions between perovskite nanocrystals to create an array of pixels with tunable emission wavelengths is proposed. This method enables the conversion of UV light, whether transmitted or reflected by an object, into the visible spectrum, thereby establishing a direct correlation between spectral and spatial information. By employing wavelength division multiplexing (WDM), the approach reduces the dimensionality of information acquisition from two dimensions to one, enhancing imaging speed. This innovation paves the way for significant advancements in all‐optical imaging and image processing, offering new possibilities for faster and more efficient imaging technologies.

The effect of internet use on the health of middle-aged and older adults: evidences from China general social survey

BackgroundAccording to the United Nations classification of aging societies, China is rapidly transitioning into a moderately aging society, facing significant challenges related to population aging. Concurrently, China is actively implementing the Internet Power Strategy and the Internet Plus action plan, which are profoundly transforming the daily lives of individuals, including their health status. However, research on the impact of internet use on the health of middle-aged and older adults has not reached a consensus. This study aims to investigate the effects of internet use on the health of middle-aged and older adults, as well as the mechanisms and heterogeneity of these effects.MethodsThis study utilized data from the China General Social Survey conducted in 2018, which includes a sample of 6470 individuals. Self-rated health, mental health, and objective health were used as key health indicators, with internet use identified as the primary independent variable. The study aims to analyze the impact of internet use on the health status of middle-aged and older adults through the application of an ordered probit model and instrumental variable methods.ResultsThe results indicate that internet use has a significant positive impact on the health of middle-aged and older adults, with significance at the 1% level. This finding has passed a series of robustness tests. Mechanism analysis reveals that information acquisition is a crucial mediating mechanism between internet use and the health of middle-aged and older adults. This suggests that increased internet use enhances information access, thereby improving health outcomes. Furthermore, heterogeneity analysis shows that the impact of internet use on health is more pronounced among individuals living in rural areas, western regions, low-income groups, and those aged under 60, compared to their counterparts in urban areas, eastern regions, higher-income brackets, and those aged 60 and above.ConclusionInternet use can improve the health of middle-aged and older adults. Therefore, government departments, senior universities, communities, and families should collaborate to actively conduct digital skills training for older adults. Additionally, efforts should be accelerated to adapt electronic products to be more elder-friendly, helping older adults overcome the digital divide and thereby reducing health inequalities among middle-aged and older groups.