Information Processing Techniques Research Articles

A bottom-up nationwide analysis of sectoral land use reveals spatial heterogeneity across the United States

The high degree of urbanization and economic development in the United States has intensified competition for land resources, leading to land use conflicts and environmental challenges. Addressing these issues first requires an exploration of high-resolution, sector-specific land use patterns. However, existing land classification studies rarely provide spatially explicit data on urban land use across all sectors at a national level. This study aims to fill this gap by analyzing land use areas across various economic sectors in the United States, particularly focusing on urban impervious surfaces. Using a bottom-up approach, we created a full-sector land use inventory, integrating Points of Interest (POI) data, road networks, building characteristics, and geographic information processing techniques. Our primary innovation lies in the development of a spatially explicit database that details sector-specific land use areas nationwide. On this robust foundation, we further explored the heterogeneity of sectoral land use eco-efficiency (SLUEE) among different sectors and states, providing supplementary insights into spatial disparities in urban land use. Our findings reveal significant spatial heterogeneity, with service sectors dominating land use and exhibiting higher SLUEE compared to goods-producing industries. Key sectors such as Professional, Scientific, and Technical Services (441064.58 ha), and Administrative and Support Services (420148.34 ha) are prominent across states according to land use area, reflecting the country's service-oriented economy. Additionally, regional disparities in SLUEE are evident, with jurisdictions like Washington D.C., New York, and California demonstrating more efficient land use. Compared with traditional land use evaluations, our model effectively offers higher spatial resolution, down to the census block level, to sectoral land use evaluation. Building on this robust database, our SLUEE analysis has uncovered notable heterogeneity between sectors and states, offering new insights into cross-sectoral and inter-state land use dynamics that can inform and promote sustainable urban planning.

AN EFFICIENT MACHINE LEARNING-BASED DETECTION AND PREDICTION MECHANISM FOR CYBER THREATS USING INTELLIGENT FRAMEWORK IN IOTS

The dangers that Internet of Things (IoT) devices pose to large corporate corporations and smart districts have been dissected by several academics. Given the ubiquitous use of IoT and its unique characteristics, such as mobility and normalization restrictions, intelligent frameworks that can independently detect suspicious activity in privately linked IoT devices are crucial. The IoTs have led an explosion in traffic through the network, bringing information processing techniques for attack detection. The increase in traffic poses challenges in detecting attacks and differentiating traffic that is harmful. In this work, we have proposed a mechanism that uses the standard algorithms in a system that is designed to detect, track, measure and identify online traffic from organizations with malignant transmission: Random Forest (RF), gradient-boosted decision trees (GBDT), and support vector machines (SVM) gives an optimal accuracy of 80.34%,87.5%, and 88.6% while the random forest-based supervised approach is 5.5% better than the previous techniques. To facilitate comparisons between training time, prediction time, specificity, and accuracy, the proposed approach leverages the NSL KDD dataset accuracy.

AN EFFICIENT MACHINE LEARNING-BASED DETECTION AND PREDICTION MECHANISM FOR CYBER THREATS USING INTELLIGENT FRAMEWORK IN IOTS

The dangers that Internet of Things (IoT) devices pose to large corporate corporations and smart districts have been dissected by several academics. Given the ubiquitous use of IoT and its unique characteristics, such as mobility and normalization restrictions, intelligent frameworks that can independently detect suspicious activity in privately linked IoT devices are crucial. The IoTs have led an explosion in traffic through the network, bringing information processing techniques for attack detection. The increase in traffic poses challenges in detecting attacks and differentiating traffic that is harmful. In this work, we have proposed a mechanism that uses the standard algorithms in a system that is designed to detect, track, measure and identify online traffic from organizations with malignant transmission: Random Forest (RF), gradient-boosted decision trees (GBDT), and support vector machines (SVM) gives an optimal accuracy of 80.34%,87.5%, and 88.6% while the random forest-based supervised approach is 5.5% better than the previous techniques. To facilitate comparisons between training time, prediction time, specificity, and accuracy, the proposed approach leverages the NSL KDD dataset accuracy.

Fundamentals and recent developments of free-space optical neural networks

Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementation of machine learning on conventional computers remains challenging due to speed and power constraints. Optical computing schemes have quickly emerged as the leading candidate for replacing their electronic counterparts as the backbone for artificial neural networks. Some early integrated photonic neural network (IPNN) techniques have already been fast-tracked to industrial technologies. This review article focuses on the next generation of optical neural networks (ONNs), which can perform machine learning algorithms directly in free space. We have aptly named this class of neural network model the free space optical neural network (FSONN). We systematically compare FSONNs, IPNNs, and the traditional machine learning models with regard to their fundamental principles, forward propagation model, and training process. We survey several broad classes of FSONNs and categorize them based on the technology used in their hidden layers. These technologies include 3D printed layers, dielectric and plasmonic metasurface layers, and spatial light modulators. Finally, we summarize the current state of FSONN research and provide a roadmap for its future development.

Enhancing Multiple Sequence Alignment with Genetic Algorithms: A Bioinformatics Approach in Biomedical Engineering

This study aimed to create a genetic information processing technique for the problem of multiple alignment of genetic sequences in bioinformatics. The objective was to take advantage of the computer hardware's capabilities and analyze the results obtained regarding quality, processing time, and the number of evaluated functions. The methodology was based on developing a genetic algorithm in Java, which resulted in four different versions: Gp1, Gp2, Gp3 and Gp4 . A set of genetic sequences were processed, and the results were evaluated by analyzing numerical behavior profiles. The research found that algorithms that maintained diversity in the population produced better quality solutions, and parallel processing reduced processing time. It was observed that the time required to perform the process decreased, according to the generated performance profile. The study concluded that conventional computer equipment can produce excellent results when processing genetic information if algorithms are optimized to exploit hardware resources. The computational effort of the hardware used is directly related to the number of evaluated functions. Additionally, the comparison method based on the determination of the performance profile is highlighted as a strategy for comparing the algorithm results in different metrics of interest, which can guide the development of more efficient genetic information processing techniques.

Non-destructive discrimination of honey origin based on multispectral information fusion technology

Accurate discrimination of honey origin is of significant importance for safeguarding consumer rights and promoting the sustainable development of apiculture. Spectroscopic techniques, as rapid, efficient, and non-destructive detection methods, have been widely applied in honey research. However, studies often focus on a single analytical technique, limiting the acquisition of comprehensive chemical information and hindering improvements in discrimination accuracy. Data fusion, an information processing technique that enhances test results' accuracy by integrating data from multiple devices, holds vast potential for various applications. Therefore, this study employed data fusion technology to investigate 142 honey samples from five different origins by combining Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) and Raman spectroscopy. Discrimination models based on single-spectral and multispectral Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM) were established using data-level and feature-level fusion strategies. Compared to single-spectral methods, multispectral data fusion significantly improved the geospatial traceability discrimination performance of the models. Particularly, the feature-level fusion strategy demonstrated significant discrimination effectiveness for honey origin. By optimizing the optimal SVM model with the Particle Swarm Optimization (PSO) algorithm, the final test accuracy reached 95.28 %, representing an 11.4 % increase over the best single-spectrum model's test accuracy. Therefore, the establishment of a PSO-SVM discrimination model using feature-level fusion technology enables rapid discrimination of honey origin, providing a reliable and stable method for non-destructive honey origin discrimination.

Information in The Three Dimensions of Time

The relationship between physics and information processing is a topic of great significance in the realm of science and technology. The laws of physics, which govern the behavior of matter and energy, also have a profound influence on the way information is processed and utilized. While the mathematics governing information processing has not extensively utilized the laws of physics, it is evident that there is potential for these laws to play a more significant role, particularly in the context of quantum systems. based on the equivalence between information and energy, we can explore how other laws of physics can effectively contribute to more efficient and advanced information processing techniques. This research sheds light on the interconnectedness of physics and information processing, and its findings have the potential to significantly enhance our ability to process information, particularly in cutting-edge quantum systems. By recognizing and leveraging the influence of physics, we can pave the way for further advancements in information processing technologies.

All-optical neuromorphic XOR and XNOR operation utilizing a photonic spiking neuron based on a passive add-drop microring resonator.

We propose a concise hardware architecture supporting efficient exclusive OR (XOR) and exclusive NOR (XNOR) operations, by employing a single photonic spiking neuron based on a passive add-drop microring resonator (ADMRR). The threshold mechanism and inhibitory dynamics of the ADMRR-based spiking neuron are numerically discussed on the basis of the coupled mode theory. It is shown that a precise XOR operation in the ADMRR-based spiking neuron can be implemented by adjusting temporal differences within the inhibitory window. Additionally, within the same framework, the XNOR function can also be carried out by accumulating the input power over time to trigger an excitatory behavior. This work presents a novel, to the best of our knowledge, and pragmatic technique for optical neuromorphic computing and information processing utilizing passive devices.

GHz photon-number resolving detection with high detection efficiency and low noise by ultra-narrowband interference circuits

We demonstrate the photon-number resolution (PNR) capability of a 1.25 GHz gated InGaAs single-photon avalanche photodiode (APD) that is equipped with a simple, low-distortion ultra-narrowband interference circuit for the rejection of its background capacitive response. Through discriminating the avalanche current amplitude, we are able to resolve up to four detected photons in a single detection gate with a detection efficiency as high as 45%. The PNR capability is limited by the avalanche current saturation, and can be increased to five photons at a lower detection efficiency of 34%. The PNR capability, combined with high efficiency and low noise, will find applications in quantum information processing technique based on photonic qubits.

Devising a technique for measuring torque of electric motors using machine vision

The object of research is the technique for measuring torques of electric motors. The main problem to be solved is the need to expand the range of informative parameters used in transmission torque converters of electric motors. This need arises from the design complications of placing the measuring devices directly on the motor shaft. As part of the study, a system was designed for determining the torques of electric motors, which combines the procedure for assessing the load on the shaft and software tools for visually determining the quantitative characteristics of such a load using machine vision. In order to acquire visual characteristics of the twisting of the shaft, a special coupling with an insert containing a liquid was designed. This coupling is able to change its shape in proportion to the load on electric motor shaft. The proposed visual control system, based on video information processing techniques, makes it possible to analyze changes in the shape of the coupling caused by the action of the torque. Features of the application of the proposed measurement technique are that there is no need to place electronic components of the measuring transducer directly on the shaft. Due to the specified features, the proposed procedure provides a solution to the problems of torque measurement in aggressive environments, which is of crucial importance for the efficiency of some specific production processes. Analysis of the visual characteristics of torque showed that the proposed approach could be applied in measuring transducers. At the same time, the results of testing the procedure confirmed that it requires the use of high-precision video recording equipment. This paves the way for the design of new, more modern, and reliable measurement systems that could be used in a wide range of industrial solutions

Magnetization spiral structure and high domain wall velocity induced by inertial effect

In ultrafast magnetism, i.e., the short time scales such as picosecond and femtosecond, we report that the spiral structure of magnetization can be formed by the inertial effect. Comparing with the case of fast magnetism in nanosecond time scale, we can obtain a higher velocity of domain wall in ultrafast magnetism due to the driving of inertial moment. There is a critical value τc of angular momentum relaxation time τ for the inertial breakdown in uniaxial ferromagnet. Under the action of the inertial effect the domain wall width shows the characteristics of increases firstly as τ<τc, and then decreasing as τ>τc. It directly leads to the positive or negative mobility for the domain wall velocity as the inertia is less than or exceeds the critical value, respectively. In the high frequency mode (i.e., high field mode), we obtain the limit domain wall velocity, i.e., V→1/ατ. The smaller the inertia or damping, the faster the domain wall moves. For the case of biaxial anisotropic ferromagnets, the domain wall width and velocity decreases with the increasing inertia term, respectively. In the presence of inertia effect, there is an another critical magnetic field hc. The domain wall width decreases with the increase of the magnetic field as h<hc, while increases as h>hc. However, the domain wall velocity shows a nearly linear increase with the magnetic field. These results indicate that the ultrafast magnetism has more advantages than fast magnetism. It also provides a good control technique for rapid information processing in the future.

Analyzing the transparency of corporate non-financial reporting

Subject. The article considers the use of ESG reporting as an information base to assess a business entity. Objectives. The aim is to carry out diagnostics of the degree of transparency of reporting information about the company's social environment. Methods. The study employs analytical and comparative techniques for information processing. Results. The paper lists objects that are reflected in modern non-financial reporting, characterizes the degree of their homogeneity. It also reveals the level of satisfaction of the information request in specific environmental and social areas, gives an integral assessment of transparency of the corporate non-financial reporting. Conclusions. It is crucial to introduce social accounting into the accounting discipline of companies. For this purpose, the study offers a system of socio-environmental responsibility.

Haar-wavelet based texture inpainting for human pose transfer

Human Image Generation is an important information processing technique. Producing realistic-looking texture is crucial for Generative Adversarial Networks (GAN) based person image generation. Existing methods follow a “downscale-upscale” strategy that source images are usually downscaled to extract features and saved the cost of storage. Meanwhile, the upscaling process is applied to recover the details based on these features. The loss of high-frequency components during the downscaling process, however, is in accordance with the Nyquist-Shannon sampling theorem, which creates an ill-posed difficulty during the upscaling process. In this paper, we design a Haar-wavelet based texture inpainting network (HWTIN) to mitigate the ill-posed problem in pose transfer task. In the downscaling process, to divide the source image into high-frequency and low-frequency contents, we construct a Haar-based Wavelet Module (HBM). In this way, We can preserve these high-frequency information in the generation process. We also design an inverse HBM (IHBM) to utilize these high-frequency information in the upscaling process. Extensive results on mainstream datasets demonstrate that HWTIN outperforms state-of-the-art (SOTA) methods quantitatively.

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Abstract This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.

CRITERIA FOR QUALITY ASSESSING OF ABSTRACT TRANSLATION

Statement of the problem. Informatization of the global space and the growing need of intercultural interaction have an impact on all spheres of human life. Techniques of fast, efficient and adequate information processing are becoming more and more practically significant. The study considers abstract translation as the most demanded and economical means of obtaining of information. In connection with this, the objective was set – to determine the criteria for quality assessing of abstract translation. Results. The article analyses the works of domestic and foreign authors, considering the criteria for determining the quality of abstract translation as well as didactic methods and stages of creating of abstract translated text as an element of linguistic, cognitive and analytical processes. The papers of participants of All-Russian student contest of abstract translation were used as a material for practical research to solve the tasks. Conclusion. The practical significance of the study lies in the considering of an abstract translation within the framework of professional bilingual communication; a qualitative and quantitative analysis of contest papers is computed; a scale of criteria for assessing the quality of abstract translation is formed.

Engines for predictive work extraction from memoryful quantum stochastic processes

Quantum information-processing techniques enable work extraction from a system&amp;apos;s inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on the one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form.

Retracted: Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

Retracted: Correlation Analysis between Early Reading Amount and Expressive Ability of Young Children Aided by Multiple Information Processing Techniques under the Heuristic Pattern

Application of graphic image processing technology in 2D game interface design

Abstract This paper uses big data information processing techniques for the underlying algorithm of interface interaction, calculates the number of game interface data points, and satisfies the hierarchical feature distribution of interface interaction. Using cross-compilation for information clustering, identifying fuzzy clustering central function feature classification, describing the underlying data statistical independent variables, and extracting graphical local feature information. Balancing constraint encoding and dictionary coefficient weighting parameters, processing edge feature images, and matching grayscale transformed image effects are conveyed. The results show that the image graphics technology has an accuracy rate of 87.25% for image recognition, the user satisfaction rate for the 2D game design interface is 78.60%, and users aged 30-40 are the largest group of game consumers. It indicates that graphic image processing technology can collect and obtain relevant information and data, thus providing important technical support for the development of the social game industry and promoting the deepening development of an information society.

Relieving youth work stress anxiety through computer information processing technique

BackgroundIn today’s fast-paced, technology-oriented business environment, anxiety among young people arising from work-related stress is a growing concern. The use of computer information processing techniques has been identified as a potential intermediary in alleviating this anxiety. Computer information processing technique has a potential mediating role in alleviating young people’s work stress and anxiety, but comprehensive intervention is still needed to integrate other factors to improve their mental health and professional life quality.Subjects and MethodsThe study involved a population of young workers exhibiting anxiety symptoms due to work-related stress. The Stanford Acute Stress Response Questionnaire (SASRQ) and the 3-min Confusion Assessment Scale (3D-CAM) were utilized to measure participants’ stress and anxiety levels pre-and-post intervention. The intervention involved the implementation of computer information processing techniques in their work routine. The outcomes were statistically analyzed using SPSS 23.0.ResultsAfter the application of computer information processing techniques, a significant reduction in SASRQ and 3D-CAM scores was observed, indicating decreased levels of work stress anxiety among the population. In addition, as a result of being able to manage tasks more efficiently, participants reported increased job satisfaction and improved work-life balance.ConclusionsThe results underscore the potential of computer information processing techniques in alleviating work-induced stress anxiety among young employees. Such techniques not only provide improved work efficiency but could also contribute to better mental health among the youth workforce.AcknowledgementScience Research Fund Project of Yunnan Education Department in 2019 (No. 2019J0999).

Pythagorean fuzzy information processing based on centroid distance measure and its applications

By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages.