Network Data Processing Research Articles

Uni-to-Multi Modal Knowledge Distillation for Bidirectional LiDAR-Camera Semantic Segmentation.

Combining LiDAR points and images for robust semantic segmentation has shown great potential. However, the heterogeneity between the two modalities (e.g. the density, the field of view) poses challenges in establishing a bijective mapping between each point and pixel. This modality alignment problem introduces new challenges in network design and data processing for cross-modal methods. Specifically, 1) points that are projected outside the image planes; 2) the complexity of maintaining geometric consistency limits the deployment of many data augmentation techniques. To address these challenges, we propose a cross-modal knowledge imputation and transition approach. First, we introduce a bidirectional feature fusion strategy that imputes missing image features and performs cross-modal fusion simultaneously. This allows us to generate reliable predictions even when images are missing. Second, we propose a Uni-to-Multi modal Knowledge Distillation (U2MKD) framework, leveraging the transfer of informative features from a single-modality teacher to a cross-modality student. This overcomes the issues of augmentation misalignment and enables us to train the student effectively. Extensive experiments on the nuScenes, Waymo, and SemanticKITTI datasets demonstrate the effectiveness of our approach. Notably, our method achieves an 8.3 mIoU gain over the LiDAR-only baseline on the nuScenes validation set and achieves state-of-the-art performance on the three datasets.

Decentralized multi-hop data processing in UAV networks using MARL

Unmanned Aerial Vehicles (UAVs) have become integral to numerous applications, prompting research towards enhancing their capabilities. For time-critical missions, minimizing latency is crucial; however, current studies often rely on sending data to ground station or cloud for processing due to their limited onboard capacities. To leverage the networking capabilities of UAVs, recent research focuses on enabling data processing and offloading within the UAV network for coordinated decision-making. This paper explores a multi-hop data offloading scheme designed to optimize the task processing and resource management of UAVs. The proposed distributed strategy uses multi-agent reinforcement learning, where UAVs, each with varying computational capacities and energy levels, process and offload tasks while managing energy consumption and latency. The agents, represented as actor-critic models, learn and adapt their actions based on current state and environment feedback. The study considers a consensus-based method to update learning weights, promoting cooperative behavior among the agents with minimum interaction. Through multiple training episodes, the agents improve their performance, with the overall system achieving faster convergence with high rewards, demonstrating the viability of decentralized data processing and offloading in UAV networks.

Simulation of Launch Pad Atomization for Military Weapon System

This study aims to optimize three critical parameters (inclination angle, hook lifting, and sparking) for a missile launching system by combining precise mathematical methods, IoT concepts, and advanced sensor technologies. The system uses an Inertial Navigation System (INS) in conjunction with gyro and proximity sensors to ensure accurate target interception by carefully determining the optimal launch angle. The Internet of Things concept is integrated effortlessly, with a clever coordinating WiFi networking, weight estimation, and sensor data processing for real-time cloud data flow. Security protocols protect the integrity of the data, and a reliable power supply system guarantees reliable operation. The project incorporates both Hot Start and Cold Start functionalities, enabling dependable launches and prompt adaptations to dynamic changes. Flexible igniting mechanisms such as Chemical, Electrical, and Electro-Chemical systems are available, and reliable and secure missile launches are prioritized with safety features like Spark Damping Inner Tube Coating and Moisture-Resistant igniting. All things considered, this project prioritizes security, dependability, and adaptability while addressing critical elements required for a sophisticated and efficient missile launching system.

Spatial lung imaging in clinical and translational settings.

For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movement, transferred cardiac pulsation, vast density range and gravitation sensitivity. However, there is extensive ongoing research in this fast-evolving field. Recent improvements in spatial imaging have allowed us to study the three-dimensional structure of the lung, providing both spatial architecture and transcriptomic information at single-cell resolution. This fast progression, however, comes with several challenges, including significant image file storage and network capacity issues, increased costs, data processing and analysis, the role of artificial intelligence and machine learning, and mechanisms to combine several modalities. In this review, we provide an overview of advances and current issues in the field of spatial lung imaging.

A Comprehensive Study of Ocular Disease Prediction Through Multi-Modal Fusion Using Deep Learning Approach

In recent years, the prevalence of ocular diseases has surged globally, necessitating advanced diagnostic methods to enhance early detection and treatment outcomes. This study presents a comprehensive analysis of ocular disease prediction through using deep learning techniques, aiming to address the limitations of traditional diagnostic methods by leveraging the strengths of multiple data sources. A key aspect of the study is the application of advanced deep learning models, specifically convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for temporal and sequential data processing. In conclusion, this research provides valuable insights into the application of multi-modal fusion in ocular disease prediction and sets the stage for future developments in integrating diverse data types for more accurate and timely diagnosis.

A miniaturized microfluidic nanoplasmonic sensor with cavity reflection enhancement for ultrasensitive molecular interaction analysis

Molecular binding kinetics quantification is pivotal in the design of antibodies, small-molecule drugs, and early diagnosis of complex disease. This study presents a portable microfluidic system, featuring a nanoplasmonic sensor with cavity reflection enhancement (NSCRE), designed for the ultrasensitive detection and analysis of molecular interactions. This device comprises liquid flow paths, generic fiber spectrometers, a versatile NSCRE sensor chip, and a novel data analysis system. Furthermore, a neural network data processing system is developed based on a deep learning model to decrease significant data fluctuations caused by air segments. This “all-in-one” platform, integrating multifunctional, easy-to-use, and renewable NSCRE biosensors, boasts detection capabilities for both slow and rapid kinetics. It can automatically monitor interactions between molecules across a broad range of molecular weights (0.259–150 kDa), allowing for real-time, ultra-sensitive, and highly selective analysis. In addition, the platform with CRE effect facilitates a 7.2-fold increase in reflection intensity signals compared to that of a sensor without CRE. The immunoglobulin G detection with a limit of detection of 73 pM based on this device, showing a 50-fold increase in sensitivity compared to previously reported values. The miniaturized automatic detection system paves a new path for the use of molecular interaction analysis in life science research, drug discovery, early diagnosis, and other fields.

Research on Network Security and Privacy Protection Technology of Autonomous Vehicle

With the rapid development of information technology and the increasing maturity of intelligent transportation systems, autonomous vehicles, as an important carrier of future travel, are gradually moving from concept to commercial application. However, the highly connected and intelligent nature of autonomous vehicles also makes them face unprecedented cybersecurity and privacy protection challenges. This paper deeply discusses the security threats and privacy disclosure risks of autonomous vehicles in network architecture, data transmission, data processing and other links, and systematically reviews the current technical means and research progress to address these challenges. This paper Outlines the data collection and transmission mechanism of autonomous vehicles, including the collection and processing of sensor data as well as the communication process between vehicles and the cloud, other vehicles and transportation infrastructure, and analyzes in detail the network security threats faced by autonomous vehicles, including remote control attacks, malware attacks, communication hijacking, etc. These threats can lead to serious consequences such as vehicle failure, data breach, or system crash. At the same time, the paper also points out the challenges faced by autonomous vehicles in terms of privacy protection, such as the risk of leakage of sensitive data such as personal location information and driving habits, as well as the risk of data abuse and sharing. In order to address the above challenges, this paper focuses on the technical research progress of cybersecurity and privacy protection of autonomous vehicles. In the aspect of network security, this paper discusses the key technical means such as data encryption, access control, intrusion detection and response system, and emphasizes the importance of security hardware and software in improving the overall security of the system. In terms of privacy protection, this paper proposes strategies such as anonymization and desensitization technology, privacy protection protocols and standards to protect users' privacy from invasion.

Retraction Note: An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Retraction Note: An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Fully Scalable Fuzzy Neural Network for Data Processing.

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted.

The potential of synthetic minority oversampling technique to enhance the precision of gender prediction: an investigation of artificial neural networks with cephalometry

BACKGROUND: When creating models utilizing artificial neural networks, the quantity of training data and the distribution of data need to be considered, particularly when making gender predictions. AIM: This study seeks to determine the potential impact of using the synthetic minority oversampling technique (SMOTE) on gender prediction using the artificial neural networks model. MATERIALS AND METHODS: The current study utilized a dataset consisting of 297 cephalometric measurements from Indonesian patients, comprising 229 samples from females and 68 samples from males. WebCeph is used to measure certain parameters, such as Sella-Nation-Point A (SNA) angle, mandibular length, mandibular angle, Sella-Glabella-Point A (SGA) angle, and diagnosis. Data processing and artificial neural networks model creation were conducted using Python. RESULTS: The gender identification accuracy of the artificial neural networks model is 87% for females and 0% for males, resulting in an overall average accuracy of 78%. When using SMOTE, the accuracy is 22%, with 0% for females and 37% for males. However, when using SMOTE and normalization, the accuracy increases to 71%, with 82% for females and 30% for males. The accuracy of normalization without SMOTE is 76%, with 86% for females and 14% for males. CONCLUSIONS: This research has proven the efficacy of SMOTE in improving the classification of male matrices. Nevertheless, this study reveals that the overall accuracy results of SMOTE are suboptimal in comparison to the absence of SMOTE and normalization. The application of data balancing strategies is necessary to achieve optimal accuracy in gender prediction when artificial neural networks, and other parameters must be applied.

Analisis Sistem Informasi Manajemen Berbasis Teknologi di MTS YPAK Cigugur

The purpose of this study was to analyze the management information system used in MTS YPAK Cigugur. The method applied in this study is qualitative descriptive analysis. Data collection techniques were carried out by interviewing the operator staff of MTS YPAK Cigugur. In addition, the researcher also conducted a literature study on a number of scientific journals related to the topic to be analyzed. The results of the study indicate that the management information system has been implemented well in MTS YPAK Cigugur. The management information system model used in MTS YPAK Cigugur is the education management information system or EMIS developed by the Ministry of Religion to assist Islamic educational institutions in managing data related to student data, educator and education data, facilities and infrastructure, and other institutional interest data. In its implementation, there are various aspects that influence it, including the procurement of adequate software such as computers and laptops, the availability of software for data processing and adequate networks to support the smooth running of the data processing process, and human resources as users who are capable and understand technology-based management information systems.

Natural language processing of social network data for the evaluation of agricultural and rural policies

Global sustainable development challenges affect the agricultural sector, and many innovations aimed at addressing these challenges have been introduced in the agri-food sector. In this complex context, new agricultural policies are being implemented in Europe. Their success depends on their potential to adapt to new realities, responding to the opinions and demands of the European population. Given the rapid rise of social media as an important part of people's daily lives, public administrations have introduced digitalization and communication strategies through social media sites. Social media can provide policymakers with large amounts of data on user opinions. Given the value of social media as a rich source of data on public views and opinions, the aim of this paper is twofold: (i) to use natural language processing (NLP) to identify the events that have led to negative or positive opinion about European Common Agricultural Policy (CAP) reform and (ii) to evaluate the ability of NLP to study users' opinions on Twitter/X. The findings show that issues such as Brexit, the European Green Deal, the role of CAP in the environment, livestock farming, food safety, and illegal practices and corruption in the distribution of CAP funds have crucial implications for the design and application of the new CAP. Moreover, the study also suggests that NLP techniques can provide opportunities to integrate agricultural policies and instruments in the agri-food sector by assessing society's opinions. Sentiment analysis, even considering its limitations, could support sound and inclusive policymaking approaches anticipating public opinion in cases of risk of social unrest.

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

Research on LiDAR point cloud data transformation method based on weighted altitude difference map

Road surface detection plays a pivotal role in the realm of autonomous vehicle navigation. Contemporary methodologies primarily leverage LiDAR for acquiring three-dimensional data and utilize imagery for chromatic information. However, these approaches encounter significant integration challenges, particularly due to the inherently unstructured nature of 3D point clouds. Addressing this, our novel algorithm, specifically tailored for predicting drivable areas, synergistically combines LiDAR point clouds with bidimensional imagery. Initially, it constructs an altitude discrepancy map via LiDAR, capitalizing on the height uniformity characteristic of planar road surfaces. Subsequently, we introduce an innovative and more efficacious attention mechanism, streamlined for image feature extraction. This mechanism employs adaptive weighting coefficients for the amalgamation of the altitude disparity imagery and two-dimensional image features, thereby facilitating road area delineation within a semantic segmentation framework. Empirical evaluations conducted using the KITTI dataset underscore our methodology’s superior road surface discernment and extraction precision, substantiating the efficacy of our proposed network architecture and data processing paradigms. This research endeavor seeks to propel the advancement of three-dimensional perception technology in the autonomous driving domain.

Low power content addressable memory designing and implementation using voltage swing self adjustable match line technique

One of the essential components of computer systems is memory. A primary hindrance in this regard is the memory speed. Content Addressable Memory (CAM) speeds up transformations and table lookups in network routers and data processing systems for hardware search engines. Parallel seeks using the CAM (Content Addressable Memory) model are often used to enhance memory performance. This paper uses the voltage swing self-adjustable match line (VSSA-ML) technique to describe low-power content addressable memory design and implementation. This project decreases Match Line (ML) power loss by reducing load capacitance and ML voltage swing. A simple ML voltage detector is proposed instead of the complex, fully different detector that allows ML voltage swings near zero. This paper presents 6 T 8×8 CAM arrays using VSSA-ML Technique using Tanner tools 45-nm technology. On the other hand, this design enhances robustness in processing variations by self-adjusting voltage swings. Implementation analysis states that the described mode 6 T 8×8 CAM design utilized fewer MOSFETs than the 8 T 8×8 CAM array.

CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.

Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model's performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).

Clustering on heterogeneous IoT information network based on meta path.

As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

Electrocardiogram identification based on data generative network and non-fiducial data processing

Nowadays, the use of biological signals as a criterion for identity recognition has gained increasing attention from various organizations and companies. Therefore, it has become crucial to have a biometric identity recognition method that is fast and accurate. In this paper, we propose a linear electrocardiogram (ECG) data preprocessing algorithm based on Kalman filters for rapid noise data filtering (wavelet transform filtering algorithm). Additionally, we introduce a generative network model called Data Generation Strategy Network (DRCN) based on generative networks. The DRCN is employed to augment training samples for convolutional classification networks, ultimately improving the classification performance of the model. Through the final experiments, our method successfully reduced the average misidentification rate of ECG-based identity recognition to 2.5%, and achieved an average recognition rate of 98.7% for each category, significantly surpassing previous achievements. In the future, this method is expected to be widely applied in the field of ECG-based identity recognition.

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Historical architecture is an important carrier of cultural and historical heritage in a country and region, and its protection and restoration work plays a crucial role in the inheritance of cultural heritage. However, the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors. Therefore, an artificial intelligence repair technology based on three-dimensional (3D) point cloud reconstruction and generative adversarial networks was proposed to improve the precision and efficiency of repair work. Firstly, in-depth research on the principles and algorithms of 3D point cloud data processing and generative adversarial networks should be conducted. Secondly, a digital restoration framework was constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes. The experimental results showed that the errors in the restoration of palace buildings, defense walls, pagodas, altars, temples, and mausoleums were 0.17, 0.12, 0.13, 0.11, and 0.09, respectively. The technique can significantly reduce the error while maintaining the high precision repair effect. This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration. It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

Enabling Decentralized Privacy Preserving Data Processing in Sensor Networks

Enabling Decentralized Privacy Preserving Data Processing in Sensor Networks