Real-time Information Processing Research Articles

Real-time Information Acquisition and Processing Method for Penetration Information Based on Multi-information Fusion

Real-time Information Acquisition and Processing Method for Penetration Information Based on Multi-information Fusion

Use of a hand-worn accelerometer to measure hand differences in Grooved Pegboard Test

Research on laterality, which pertains to the preference for using one hand over the other in daily activities, has recently been a growing topic of investigation. Studies demonstrate that performance differences between the right and left hands arise from distinct strategies in utilizing information for movement execution. To conduct a more comprehensive analysis of these differences, we aimed to investigate various measures using a wrist accelerometer during the execution of a traditional tool to assess manual performance. The proposed hypothesis suggested that tasks performed with the right hand would exhibit shorter movement times due to more efficient real-time information processing. In contrast, tasks performed with the left hand would show shorter reaction times due to movement planning proficiency. Furthermore, we hypothesized that increased task complexity would make these differences more pronounced. The study revealed that, in the less complex task, the right hand outperformed the left hand in execution speed, whereas the left hand demonstrated faster reaction times in the more complex task. The task complexity highlighted the differences, emphasizing the impact of task demands on hand specialization. Using an accelerometer provided valuable insights, indicating potential avenues for refining assessment tools and analyzing manual control. Keywords: motor control, hemispheric specialization, laterality, hand function, dexterity.

Real-time information processing via volatile resistance change in scalable protonic devices

Biological neural systems operate on multiple time scales, enabling real-time interaction with their environment. However, replicating this in electronic systems is challenging, as scalable devices that operate on similar time scales, particularly from seconds to minutes, are lacking. This study addresses this gap by exploiting proton dynamics to achieve volatile resistance changes over long time scales, and developed a neuromorphic system that can predict biomedical data, such as blood glucose levels, in real time. By applying a low voltage (below 1 V) to a two-terminal device, the Pd electrode hydrogenates or dehydrogenates the mixed conductor WO3, resulting in significant changes in electronic resistance. The device is scalable due to the uniform proton distribution, leading to high impedance and minimal power consumption. Utilizing these volatile protons for short-term memory in an Echo State Network (ESN), this approach demonstrates low-power, efficient real-time information processing, paving the way for future neuromorphic computing applications.

Architecture and iot security systems based on fog computing

The subject of the study is is the security architecture of the Internet of Things (IoT) based on fog computing, which allows providing efficient and secure services for many IoT users. The goal is to investigate the security architecture for IoT systems based on fog computing. To achieve the goal, the following tasks were solved: the concept of fog computing is proposed, its architecture is considered and a comparative analysis of fog and cloud computing architectures is made; the principles of designing and implementing the architecture of a fog computing system are outlined; multi-level security measures based on fog computing are investigated; and the areas of use of fog computing-based Internet of Things networks are described. When performing the tasks, such research methods were used as: theoretical analysis of literature sources; analysis of the principles of designing and implementing the security architecture of the Internet of Things; analysis of security measures at different levels of the architecture. The following results were obtained: the architecture of fog computing is considered and compared with the cloud architecture; the principles of designing and implementing the architecture of fog computing systems are formulated; multi-level IoT security measures based on fog computing are proposed. Conclusions: research on IoT security systems based on fog computing has important theoretical implications. The fog computing architecture, in contrast to the cloud architecture, better meets the demand for high traffic and low latency of mobile applications, providing more advantages for systems that require real-time information processing. When designing and implementing the architecture of fog computing systems, the factors of memory capacity, latency, and utility should be taken into account to effectively integrate fog technologies with IoT. To ensure a high level of system security, multi-level security measures should be implemented using both software and hardware solutions.

The role of coarticulatory tonal information in Cantonese spoken word recognition: an eye-tracking study

Abstract Previous eye-tracking studies suggest listeners closely track coarticulatory segmental information to facilitate spoken word recognition. In tonal languages such as Cantonese, the F0 value of high-level and rising tones is higher when preceding a low tone than preceding a high tone. Given the tonal coarticulation effect in Cantonese, this study used a printed-word version of the visual-world eye-tracking paradigm to investigate Macau Cantonese listeners’ real-time processing of coarticulatory information through two types of word pairings, in which target words and competitor words are either matched or mismatched in coarticulatory pattern (i.e., preceding a low or high tone). The results of the growth curve analysis on the difference between target and competitor fixations show that a fixation advantage of target words increases more quickly over time in the mismatch condition than in the match condition. The overall findings suggest that native listeners closely attend to the coarticulatory tonal information to facilitate lexical activation during moment-by-moment processing. Since the mechanisms of the coarticulatory effect may differ between segments and lexical tones, this research calls for further eye-tracking studies that manipulate coarticulation cues in segmental and suprasegmental domains.

Real-time procedure information sharing as a means to reduce perioperative anxiety in families of children undergoing elective surgery - a randomized controlled study

ObjectiveTo investigate whether the surgical process information sharing system could alleviate the parental anxiety during a pediatric selective operation.DesignRandomized controlled trial.MethodsA questionnaire survey was conducted one day before surgery for the enrolled participants. Family members assigned to the intervention group received real-time process information sharing through service reminders during the surgical period, while the control group received standard perioperative education. The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate sleep quality during the perioperative period, and the State of Cohesion-13 Scale (SOC-13) and Self-rating Anxiety Scale (SAS) were used to assess anxiety levels. Satisfaction levels during the perioperative period were assessed through a follow-up survey conducted one day after surgery.ResultsThe intervention group showed better scores in terms of PSQI, SOC-13, SAS, and postoperative satisfaction levels at various time points compared to the control group, with statistically significant differences observed (P < 0.05).ConclusionReal-time process information sharing is effective in reducing perioperative sleep disorders and anxiety among family members of pediatric patients, as well as improving satisfaction levels. This approach not only establishes a process and mechanism for effective doctor-patient communication but also helps implement continuous perioperative care, thereby optimizing internet healthcare services.

A Bayesian network method using transfer learning for solving small data problems in abnormal condition diagnosis of fused magnesia smelting process

Intelligent abnormal condition diagnosis is key to the safe and stable operation of the fused magnesia smelting process (FMSP). Existing studies have applied the Bayesian network (BN) to the FMSP with sufficient data. However, learning an accurate BN model for a new FMSP using small data isn't easy. Furthermore, the unreasonable diagnosis may seriously affect product quality and security threats. To this end, we present a novel BN transfer learning (BNTL) method to resolve this abnormal condition diagnosis problem using small data. The abnormal condition diagnosis model is established offline based on the proposed BNTL method, and the occurrence or extent of abnormal conditions is identified online based on the diagnosis model and real-time process information. The BNTL involves structure and parameter transfer learning. Among them, structure transfer learning is based on score and search. A new scoring function is proposed to evaluate the local structure of the candidate target domain by weighted integration of source and target domain data based on similarity. The search process is based on the genetic algorithm with other constraints that remove loops and control the number of parent nodes. After learning the structure, parameter transfer learning is done according to a new similarity evaluation and fusion algorithm. The public network is first used to evaluate the BNTL method. Then, it is utilized to build the abnormal condition diagnosis model of the FMSP using small data. Experimental results show that the recognition accuracy is significantly improved compared with other advanced methods in a limited data environment.

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.

Artificial Tactile Perception System Based on Spiking Tactile Neurons and Spiking Neural Networks.

The artificial tactile perception system of this work utilizes a fully connected spiking neural network (SNN) comprising two layers. Its architecture is streamlined and energy-efficient as it directly integrates spiking tactile neurons with piezoresistive sensors and Pt/NbOx/TiN memristors as input neurons. These spiking tactile neurons possess the ability to perceive and integrate pressure stimuli from multiple sensors and encode the information into rate-coded electrical spikes, closely resembling the behavior of a biological tactile neuron. The system's real-time information processing capability is demonstrated through an artificial perceptual learning system that successfully encodes and decodes the Morse code; the artificial perceptual learning system accurately recognizes and displays 26 English letters. Furthermore, the artificial tactile perception system is evaluated for the recognition of the MNIST data set, achieving a classification accuracy of 85.7% with the supervised spiking-rate-dependent plasticity learning rule. The key advantages of this artificial tactile perception system are its simple structure and high efficiency, which contributes to its practicality for various real-world applications.

Cloud material handling systems: a cyber-physical system to enable dynamic resource allocation and digital interoperability

AbstractThe existing logistics practices frequently lack the ability to effectively handle disruptions. Recent research called for dynamic, digital-driven approaches that can help prioritise allocation of logistics resources to design more adaptive and sustainable logistics networks. The purpose of this study is to explore inter-dependencies between physical and digital assets to examine how cyber-physical systems could enable interoperability in logistics networks. The paper provides an overview of the existing literature on cyber-physical applications in logistics and proposes a conceptual model of a Cloud Material Handling System. The model allows leveraging the use of digital technologies to capture and process real-time information about a logistics network with the aim to dynamically allocate material handling resources and promote asset and infrastructure sharing. The model describes how cloud computing, machine learning and real-time information can be utilised to dynamically allocate material handling resources to product flows. The adoption of the proposed model can increase efficiency, resilience and sustainability of logistics practices. Finally, the paper offers several promising research avenues for extending this work.

Digital enabled agility: Industry 4.0 unlocking real-time information processing, traceability, and visibility to unleash the next extent of agility

ABSTRACT Digital enabled agility reflects the transformative outset of an organisation to first upgrade its business processes digitally towards facilitating real-time information processing/capabilities. Second, tapping from real-time capabilities, digital enabled agility maximises an agile response's efficacy in enhancing the sophistication of rapidity, flexibility, and sensing involved. In this backdrop, driven by newer IT infrastructure and disruptive technology, this research accentuates on Industry 4.0 digitalisation to understand how a manufacturer can gain digital enabled agility. An empirical survey of manufacturers within the premise of Industry 4.0 is conducted. Drawn from the findings, the capabilities of traceability and visibility can be facilitated by Industry 4.0 that would improve information sharing and information quality in real-time. These capabilities are key propellants to unlock the digital extent of agility. This research however details further how this scope of agility is an extension to that of the scope of typical agility in terms of its ability to handle expansive information processing needs and dynamic coordination/resource orchestration. Insights are weaved under the purviews of (a) organisational information processing view and (b) the manufacturer who is coordinating contemporary dynamic production and allied value chain requirements.

Alternative to mean and least squares methods used in processing the results of scientific and technical experiments

Increasing the complexity and size of systems of various nature requires constant improvement of modeling and verification of the obtained results by experiment. It is possible to clearly conduct each experiment, objectively evaluate the summaries of the researched process, and spread the material obtained in one study to a series of other studies only if they are correctly set up and processed. On the basis of experimental data, algebraic expressions are selected, which are called empirical formulas, which are used if the analytical expression of some function is complex or does not exist at this stage of the description of the object, system or phenomenon. When selecting empirical formulas, polynomials of the form: у = А0 + А1х+ А2х2+ А3х3+…+ Аnхn are widely used, which can be used to approximate any measurement results if they are expressed as continuous functions. It is especially valuable that even if the exact expression of the solution (polynomial) is unknown, it is possible to determine the value of the coefficients An using the methods of mean and least squares. But in the method of least squares, there is a shift in estimates when the noise in the data is increased, as it is affected by the noise of the previous stages of information processing. Therefore, for real-time information processing procedures, a pseudo-reverse operation is proposed, which is performed using recurrent formulas. This procedure is a procedure of successive updating (with a shift) along the columns of the matrix of given sizes and pseudo-reversal at each step of information change. This approach is straightforward and takes advantage of the bounding method. With pseudo-inversion, it is possible to control the correctness of calculations at each step, using Penrose conditions. The need for pseudo-inversion may arise during optimization, forecasting of certain parameters and characteristics of systems of various purposes, in various problems of linear algebra, statistics, presentation of the structure of the obtained solutions, to understand the content of the incorrectness of the resulting solution, in the sense of Adomar-Tikhonov, and to see the ways of regularization of such solutions.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

A Large-Scale Photonic CNN Based on Spike Coding and Temporal Integration

The real-time massive-data intelligent information processing tasks highlight a vital requirement for the novel, intelligent optimization hardware. Convolutional neural networks are highly capable of extracting the hierarchical feature map and enhancing recognition accuracy, with photonics-enabled implementations drawing considerable interest. Here, we propose a large-scale and reconfigurable photonic convolutional neural network (PCNN), based on a hardware-friendly distributed feedback laser diode (DFB-LD). Our approach applies biological time-to-first-spike coding to a DFB-LD neuron to perform a temporal convolutional operation (TCO) for image processing tasks. In PCNN, experimental results demonstrate that we successfully implement the TCO to extract the image features with convolutional kernels of size 11×11. Furthermore, we investigate the temporal pulse shaping of a DFB-LD neuron to build a densely-connected fully connected layer, which synaptic weights can be rapidly adjusted at a rate of 5 GHz, achieving full MNIST and Fashion-MNIST benchmark image classification tasks, with classification accuracies of 98.56% and 87.48%, respectively. This work highlights the potential of neuron-like optical analog computing platforms for real-time and more complex intelligent processing networks.

Design of an ALU in QCA Technology Dedicated to Intelligent Edge Computing Systems

Following the considerable development of big data and the need for real-time information processing, the recent computing paradigm changed from centralized intelligence on cloud computing to the distributed intelligence on Edge Computing. On the algorithm layer, Artificial Intelligence (AI) has been applied in different areas related to intelligent-edge applications. In this type of application, consumption and speed present essential challenges when designing hardware-level architecture on the embedded systems. In particular, an Edge Computing System (ECS) must be an energy efficient, compact, and fast component integrated into edge devices. Miniaturization has been happening at a steady pace for the last years. However, the MOS transistor size has been slowly reaching the physical limit. In this paper, we propose a Quantum Cellular Automata clock-phase-based technique for the design of the proposed ALU module. The obtained results using the QCA architecture designer exhibit better performances over the existing designs in terms of size, cell number, cost, latency, and power consumption. We have also used QCAPro software for power consumption estimation. The results show the effectiveness of the design in terms of cost and power.

Are medical oncologists ready for the artificial intelligence revolution? Evaluation of the opinions, knowledge, and experiences of medical oncologists about artificial intelligence technologies.

The use of artificial intelligence technologies (AIT) in medicine is increasing worldwide. In this study, it was aimed to evaluate the experiences, opinions, and future expectations of medical oncologists on artificial intelligence (AI). After the reliability and validity analyses were carried out by a pilot study, the main online questionnaire was sent to the members of the "Turkish Society of Medical Oncology" mail group byan invitation e-mail. The anonymized responses of the participants were analyzed. The median age of the 156 participants was 36 (34-43) years and half (51%) were male. Most (45%) were fellows. Forty-six percent were working in university hospitals, 56% were visiting 20-40 patients a day. Medical oncologists' view of AIT was mostly positive (78%). However, some (especially women) had doubts about the reliability of AI (44%) and the establishment of its ethical/legal basis (49%). Sixty-five percent of the participants had no/superficial knowledge about AI. More than half (55%) had never used AI-based applications in their academic or clinical work. However, unlike now, 80% of the participants believed that they would use AIT frequently in their practice in thefuture and it would be beneficial. The most anticipated (81%) benefit was real-time information processing and real-time access to big data. Sixty-two percent believed that information about AI should be in the education curriculum. The vast majority of respondents (79%) thought that AI would not completely replace medical oncologists in thefuture. Some differences were found in the perception and experience of oncologists according to age, gender, title, and the number of patients examined per day. About AI, the general opinion of medical oncologists was positive, but their level of knowledge and use was low. However, they thought they would use it frequently in future and needed training.

High-frame-rate reconfigurable diffractive neural network based on superpixels.

The existing implementations of reconfigurable diffractive neural networks rely on both a liquid-crystal spatial light modulator and a digital micromirror device, which results in complexity in the alignment of the optical system and a constrained computational speed. Here, we propose a superpixel diffractive neural network that leverages solely a digital micromirror device to control the neuron bias and connection. This approach considerably simplifies the optical system and achieves a computational speed of 326 Hz per neural layer. We validate our method through experiments in digit classification, achieving an accuracy of 82.6%, and action recognition, attaining a perfect accuracy of 100%. Our findings demonstrate the effectiveness of the superpixel diffractive neural network in simplifying the optical system and enhancing computational speed, opening up new possibilities for real-time optical information processing applications.

The Effects of Social Status and Imposition on the Comprehension of Refusals in Chinese: An ERP Study.

This study aims to examine how real-time processing of information about the social status of interlocutors (high vs. low) and the imposition of making a refusal by manipulating the indirectness of invitation forms (declining direct invitations vs. declining indirect invitations) affects the interpretation of refusals in Chinese. The event-related potentials results showed that high-status invitees who decline invitations from low-status inviters elicited weaker N400 effects followed by late mitigated negative effects, while high imposition refusals elicited stronger N400 effects followed by increased late negativities. The two factors of social status and imposition functioned independently during the comprehension of refusal utterances. These findings suggest that individuals take the social status of interlocutors and the imposition of making a refusal into consideration as an utterance unfolds, while face-threatening contexts create inferential difficulties for reinterpreting the pragmatic implications of an utterance.

Emerging Memtransistors for Neuromorphic System Applications: A Review.

The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an "all-in-one" low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), HfxZr1-xO2(HZO), In2Se3, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented.

Spectrally Resolved Hong-Ou-Mandel Interferometry with Discrete Color Entanglement

Hong-Ou-Mandel (HOM) interferometry is a powerful quantum metrology technique to reconstruct the surface-depth profile of transparent samples with complementary benefits of measurement precision and experimental robustness. It is generally assumed that a trade-off exists between the standard HOM interferometry sensitivity and its ambiguity-free dynamic range. Here we challenge this ``well-known'' assumption through the implementation of spectrally resolved HOM interferometry with discrete color entanglement, and show that the well-separated frequencies embedded in a quantum state suffices to achieve great advantages in enhancing the temporal resolution but without limiting its ambiguity-free dynamic range. Backed by the Fisher-information analysis, spectrally resolved coincidence detection is an optimal measurement strategy as it enables us to recover the quantum Cram\'er-Rao bound. These results may significantly facilitate the use of spectrally resolved quantum interference for real-time quantum information processing, such as ultrafast phase transition and spectral-temporal photography.