Sensor Data Processing Research Articles

Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks.

This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8-99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.

АНАЛІЗ ПІДХОДІВ ДО ВИДАЛЕННЯ ХМАРНОСТІ НА КОСМІЧНИХ ЗНІМКАХ ДИСТАНЦІЙНОГО ЗОНДУВАННЯ ЗЕМЛІ

The modern development of space technologies and remote sensing creates unique opportunities for solving problems in many areas, including the military. Remote sensing imagery often plays a key role in decision-making at all levels of military command, so one of the most important tasks in this context is cloud detection and extraction. This is an important stage of remote sensing data processing aimed at reconstructing information hidden by clouds. The article is devoted to the analysis of different approaches to cloud removal and improvement of the data quality. The approaches based on the use of various image processing algorithms (traditional approaches) have certain limitations associated with the frequent loss of useful information. Special attention is paid to deep learning methods, which have gained popularity in solving cloud removal problems. Deep Neural Networks show great potential for recovering information on satellite images that is hidden by clouds. This paper discusses various Deep Neural Networks architectures, such as convolutional neural networks, conditional generative adversarial networks, and their modifications. Their advantages and disadvantages are also considered. The use of such methods is more accurate and efficient compared to traditional image processing methods, as neural networks can adapt to various conditions and types of images. The analyzed disadvantages of fusing purely optical data led to the conclusion that the best approach to solving the problem of removing clouds from satellite images would be to combine optical and radar data. Despite the complexity of such an approach, it can show the greatest efficiency in solving the problem considered in this article. The challenges and prospects for further improvement of cloud removal methods on satellite images are considered. In particular, the use of artificial intelligence algorithms for automatic cloud detection and removal, as well as the need to create standardized methods for comparing and evaluating the effectiveness of different approaches. Keywords: satellite imagery; remote sensing; cloud cover; neural networks.

Comparative Study on BEV Vision and LiDAR Point Cloud Data Fusion Methods

With the gradual maturity of autonomous driving technology, efficient fusion and processing of multimodal sensor data has become an important research direction. This study mainly explores the strategy of integrating BEV (Bird's Eye View) vision with LiDAR point cloud data. We evaluated the performance and applicability of each of the three main data fusion methods through in-depth comparison: early fusion, mid-term fusion, and late fusion. First of all, we summarize the working principle and data characteristics of BEV vision and LiDAR, and emphasize their key roles in auto drive system. Then, the theoretical basis and implementation methods of the three fusion strategies were described in detail. The experimental results show that different fusion strategies exhibit their own advantages in different application scenarios and requirements. For example, early fusion performs well in high-precision tasks, but has a high demand for computing resources. And mid-term fusion is more suitable in scenarios with high real-time requirements. Overall, this study provides in-depth insights and practical suggestions on the fusion of BEV vision and LiDAR data in the field of autonomous driving, laying a solid foundation for future research and applications.

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning.

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.

Special Issue on Autonomous Robotics Challenge

The Tsukuba Challenge and the Nakanoshima Robot Challenge are both technical challenges in which mobile robots run autonomously in real outdoor environments. They have been held almost every year since 2007 and 2018, respectively, and many robots have participated in these public experiments. The autonomous navigation of a mobile robot in the real world, that is, not on test tracks but in environments normally used by everyday people, poses a great many challenges. The robots have to deal with environments that change with the time of day, the weather, the season, etc., and they have to deal with unexpected stationary obstacles as well as moving ones, including people, bicycles, etc. These days, demonstration tests of delivery robots are being conducted in many places, but there are still many problems that need to be solved. In this special issue, we have gathered papers detailing the insights gained from running mobile robots in the two outdoor experiments, the Tsukuba Challenge and the Nakanoshima Robot Challenge. To run a robot with a high success rate in a real environment, it is very important to devise the robot configuration, the sensor data processing, and the behavior control based on the knowledge gained from many experiences. We hope that sharing the successes and failures in the papers in this special issue will lead to further technological improvements in the future.

A Control Architecture for Developing Reactive Hybrid Remotely Operated Underwater Vehicles

This article introduces a control architecture designed for the development of Hybrid Remotely Operated Underwater Vehicles. The term ”Hybrid” characterizes Remotely Operated systems capable of autonomously executing specific operations. The presented architecture maintains teleoperation capabilities while enabling two fully autonomous applications. The approach emphasizes the implementation of reactive navigation by exclusively utilizing data from a Mechanical Scanned Imaging Sonar for control decisions. This mandates the control system to solely react to data derived from the vehicle’s environment, without considering other positioning information or state estimation. The study involves transforming a small-scale commercial Remotely Operated Underwater Vehicle into a hybrid system without structural modifications, and details the development of an intermediate Operational Control Layer responsible for sensor data processing and task execution control. Two practical applications, inspired by tasks common in natural or open-water aquaculture farms, are explored: one for conducting transects, facilitating monitoring and maintenance operations, and another for navigating toward an object for inspection purposes. Experimental results validate the feasibility and effectiveness of the authors’ hypotheses. This approach expands the potential applications of underwater vehicles and facilitates the development of Hybrid Remotely Operated Underwater Vehicles, enabling the execution of autonomous reactive tasks.

Research on combination of periodic inspection and data-driven maintenance system of turbofan engine

In the field of aviation, turbofan engine is a crucial high-precision component of an aircraft which requires rigorous maintenance to ensure the running stability, safety, and efficiency. To reduce the high cost of general maintenance, the system of combination of periodic inspection and Data-driven maintenance is a meaningful research trend. In this pattern, the engine's condition study over sensor data serves as the foundation for the maintenance, to meet the safety requirements, improved sensor data processing and interpretation and precise prediction model plays important role. In this study, Long Short-Term Memory (LSTM) architecture was used to estimate RUL (remaining useful life) and support periodic inspection. The experiments were taken by using the dataset of FD001 and FD003 in Turbofan engine degradation dataset that contains operating and degradation information of several turbofan engines. After being analysed by different evaluation methods, the algorithm is capable to discover the features that hidden among sensor data, which indicates that the model has promising results for predictive maintenance. Therefore, the Data-driven maintenance structure that integrated with LSTM architecture is proposed as an application method. Furthermore, considering all aspects of the real application, management tools, Kubernetes and Kubeflow are introduced in this paper. Finally, regularly scheduled inspection is introduced, with the help of estimated RUL, inspection can be applied by parts rather than removing engine for overhaul and replacement of all parts including parts in good condition.

Digital twin modeling method of the temperature field of thermo-compression bonding blade based on generative adversarial networks

In this paper, a novel method of using deep learning to construct a digital twin of the thermos-compression bonding blade`s temperature field is presented. Usually, a heat transfer model based on numerical analysis can calculate the heat distribution in the welding process, but its complex modeling process and heavy computational effort are not appropriate for the deployment of the digital twin concept. The approach proposed in this paper is distinctive because it directly converts the collected process sensor data into the bonding blade's temperature field images and the bonding surface's temperature distribution curves for monitoring the welding process. This is achieved by training the generator and discriminator of the conditional depth convolutional generative adversarial network separately. The network is then capable of predicting the bonding blade's heat distribution within a certain range of welding parameters. Compared to methods based on numerical simulation, this approach is far more effective. Training and testing datasets were created to assess the suggested method's efficacy. Multi-scale structural similarity index, structural similarity index, peak signal-to-noise ratio, difference hash and mean absolute error were used to evaluate the simulations' quality. The results show that the simulation's results and the actual data agree quite well. The temperature changes that occur during the welding process may be quickly and accurately simulated using the proposed technology.

Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks

Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data.

Improving Accuracy of Sensor Data by Frequent Pattern Mining Algorithm Using Edge Computing

Sensor data plays a crucial role in various applications, including industrial automation, environmental monitoring, and healthcare. However, the accuracy of sensor data can be adversely affected by factors such as noise, latency, and data transmission issues in existing systems. This study focuses on identifying the disadvantages associated with current sensor data collection and analysis methods and explores the use of frequent pattern mining to enhance data accuracy. The research presents a comprehensive overview of Edge computing in conjunction with sensor systems and the Internet of Things, highlighting the complexities in processing sensor data using conventional methods and the advantages of employing frequent pattern mining. The study concludes that the utilization of frequent pattern mining in edge sensor data processing offers optimized response time, resource utilization, and better scalability. It is also capable of handling the massive amount of data generated from sensors and mobile devices in the Internet of Things.

Optimization Water Conservation Through IoT Sensor Implementation At Smartneasy Nusantara

The use of IoT sensors in technology is a fascinating research subject due to the possibility of real-time monitoring of water usage and temperature effects. Developing tools and systems that make observations of water usage through Max6675 sensors can optimize data collection and processing through remote monitoring. Utilizing the IoT technique, the ESP8266 WeMOS D1 R2 microcontroller and the Max6675 temperature sensor are utilized to regulate the water pump via a relay. This approach enhances water sustainability for plant assumptions based on the predetermined temperature. The device activates the water pump when the temperature surpasses the standard level at the observation site. The outcome indicated that this device operates accordingly with a 100% success rate. The device was determined to function effectively by activating the water pump based on temperature. Additionally, it can provide real-time monitoring data and process sensor data for analysis.

Cost-Sensitive Decision Support for Industrial Batch Processes

In this work, cost-sensitive decision support was developed. Using Batch Data Analytics (BDA) methods of the batch data structure and feature accommodation, the batch process property and sensor data can be accommodated. The batch data structure organises the batch processes’ data, and the feature accommodation approach derives statistics from the time series, consequently aligning the time series with the other features. Three machine learning classifiers were implemented for comparison: Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machine (SVM). It is possible to filter out the low-probability predictions by leveraging the classifiers’ probability estimations. Consequently, the decision support has a trade-off between accuracy and coverage. Cost-sensitive learning was used to implement a cost matrix, which further aggregates the accuracy–coverage trade into cost metrics. Also, two scenarios were implemented for accommodating out-of-coverage batches. The batch is discarded in one scenario, and the other is processed. The Random Forest classifier was shown to outperform the other classifiers and, compared to the baseline scenario, had a relative cost of 26%. This synergy of methods provides cost-aware decision support for analysing the intricate workings of a multiprocess batch data system.

Obstacle Detection and Avoidance Using Ultrasonic Sensors in Autonomous Robots

Autonomous robots have recently gained traction in a variety of industries due to their efficiency and possible cost savings. However, assuring the safety and dependability of these robots is critical, especially in terms of obstacle detection and avoidance. Ultrasonic sensors, which are known for their low cost, user-friendliness, and dependability, have emerged as a popular and practical solution to this problem. In this paper, the author offer a framework for using numerous ultrasonic sensors in autonomous robots to detect and avoid obstacles.In our approach, real-time sensor data processing is used to correctly identify and quantify the distances to obstacles, which subsequently guides the robot's path adjustment using obstacle avoidance algorithms. The findings show that our proposed framework for multiple ultrasonic sensor-based range and obstacle avoidance is capable of identifying and avoiding obstacles in a variety of situations. Furthermore, this study conducted a comparison analysis between our method and standard single ultrasonic sensor-based obstacle identification and avoidance approaches, indicating that our approach is more accurate, reliable, and robust.This method ensures safe and dependable navigation in a variety of applications, including mobile robots, autonomous vehicles, and drones. Overall, our research advances the development of low-cost, highly dependable autonomous robots for obstacle recognition and avoidance, allowing them to navigate difficult settings.

A Model of a Smart Garden Using the Integration of a Set of Components

The purpose of this paper is to describe an experiment in the design and construction of an intelligent irrigation system based on the integration of ready-made programmed components and programmable ones, which, if necessary, will provide watering of plantations. This paper presents a model of an integrated multi-component architecture for a smart garden, which is based on the combination of various components and techniques in small-scale urban garden irrigation by using Internet of Things (IoT) technologies. Automation is related to planning based on sensor data processing, determining when to activate water supply. Unlike manufacturers who offer "smart irrigation" solutions with all the automated components needed to build a complete intelligent system, our approach is different. We combine components from various manufacturers to create an independent irrigation system. The challenge lies in ensuring these components can seamlessly work together, processing automation instructions through external software, and storing data on a remote server. The described model represents an innovative solution that enhances the functionality and sustainability of urban gardens by combining an understanding of the gardens' needs and effective resource management, contributing to an improved quality of life for citizens and the sustainable use of natural resources.

A Big Data System Architecture to Support the Monitoring of Paved Roads

Today, everything is connected, including the exchange of data and the generation of new information. As a result, large amounts of data are being collected at an ever-increasing rate and in a variety of forms, a phenomenon now known as Big Data. Recent developments in information and communication technologies are driving the generation of significant amounts of data from multiple sources, namely sensors. In response to these technological advances and data challenges, this paper proposes a Big Data system architecture for paved road monitoring and implements part of this architecture on a section of road in Portugal as a case study. The challenge in the case study architecture is to collect and process sensor data in real time, at a rate of 500 records per second, producing 15 GBytes of data per day, using a real-time data stream for real-time monitoring and a batch data stream for deeper analysis. This allows users to obtain instant updates on road conditions such as the number of vehicles, loads, weather, and pavement temperatures on the road. They can monitor what is happening on the road in real time, receive alerts, and even gain insight into historical data, such as analysing the condition of structures or identifying traffic patterns.

Incremental density clustering framework based on dynamic microlocal clusters

With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors’ streaming data analysis.

Computational Intelligence in Remote Sensing

With the development of Earth observation techniques, vast amounts of remote sensing data with a high spectral–spatial–temporal resolution are captured all the time, and remote sensing data processing and analysis have been successfully used in numerous fields, including geography, environmental monitoring, land survey, disaster management, mineral exploration and more [...]

Land Use and Land Cover Classification Meets Deep Learning: A Review.

As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth's surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification.

Design of Sensor Data Processing Software for the ISO 23150 Standard: Application to Autonomous Vehicle Software

The ISO 23150 standard defines the logical interface between sensors and fusion units. To apply this standard to actual vehicles, software is required to convert sensor data into ISO 23150-compliant sensor data. In this study, we developed sensor data processing software to provide ISO 23150-compliant sensor data to autonomous vehicle software. The main contributions of this study are as follows: First, the safety of the software is considered, and its structure and error detection method are designed to minimize the impact of errors. Second, the software structure is in accordance with the ISO 23150 standard, and a framework structure is designed with convenience in mind. Third, we considered its compatibility with adaptive AUTOSAR by designing a data delivery service using SOME/IP communication. We evaluated the security and data delivery delay of the software on a controller used in an actual vehicle and noted high security and real-time performance. The evaluation results demonstrated the feasibility of this method for real-world vehicles. Our study can serve as a basis for advancing autonomous driving technology in the context of ensuring software safety.

Mamdani vs. Takagi-Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models.

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi-Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi-Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi-Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.