Sensor Inputs Research Articles

Enhancing indoor temperature mapping: High-resolution insights through deep learning and computational fluid dynamics

This paper examines the temperature distribution in a closed, rectangular room equipped with an air conditioning system, employing a computational fluid dynamics model to simulate a virtual thermal camera that captures detailed temperature snapshots. A super-resolution framework enhances the postprocessing of these results. Specifically, convolutional neural networks, trained on simulation data, are used to accurately model temperature fields' high-resolution spatial and temporal evolution. The model demonstrates strong performance by accurately reconstructing temperature profiles from low-resolution inputs obtained from filtering data obtained using high-resolution numerical simulations, with quantitative metrics indicating acceptable accuracy for resolutions reduced by up to 50 times. This effectively aligns with ground truth profiles under various conditions. These results underscore the super-resolution model's potential to transform environmental monitoring in smart buildings and complex structures by generating high-resolution thermal maps from low-resolution cameras or limited sensor input. This approach offers a fast, cost-effective, and reliable method for accurately modeling thermal dynamics within the turbulent flow environments of interior spaces.

Sistem Monitoring Dan Pendeteksi Kebersihan Udara Pada Kandang Peternakan Sapi Berbasis Internet Of Things (IoT)

Milk consumption and the need for beef cattle for food purposes or sacrificial purposes continue to increase from year to year. This increase is in line with the increasing level of the economy and awareness of the need for nutritious food. However, the increase in demand still faces many production constraints due to the difficulties faced by farmers, the decline in cattle production from year to year in Indonesia. This causes Indonesia to have to import beef for 35.95% of the total national beef consumption. With many factors that affect the decline in the production rate of dairy cows or beef cattle every year, one of them is air pollution and poor air quality in cages. Along with the development of technology, a monitoring and detection system for air cleanliness in cattle pens was created, using the MQ-7 sensor as the concentration of carbon monoxide, the MQ-135 sensor as the concentration of ammonia gas, the MQ-2 sensor as the smoke detection, and When the air pollution value of each sensor input is according to the air pollution standard index (ISPU), the outputs namely indicator lights, buzzers and blowers will be on, then there will be temporary air neutralization in the cage. This tool is made easier by internet of things technology, so that it can send notifications via telegram and can see the level of air pollution levels through the web.

In Vivo Evaluation of Two Hemorrhagic Shock Resuscitation Controllers with Non-Invasive, Intermittent Sensors.

Hemorrhage is a leading cause of preventable death in military and civilian trauma medicine. Fluid resuscitation is the primary treatment option, which can be difficult to manage when multiple patients are involved. Traditional vital signs needed to drive resuscitation therapy being unavailable without invasive catheter placement is a challenge. To overcome these obstacles, we propose using closed-loop fluid resuscitation controllers managed by non-invasive, intermittent signal sensor inputs to simplify their use in far-forward environments. Using non-invasive, intermittent sensor controllers will allow quicker medical intervention due to negating the need for an arterial catheter to be placed for pressure-guided fluid resuscitation. Two controller designs were evaluated in a swine hemorrhagic shock injury model, with each controller only receiving non-invasive blood pressure (NIBP) measurements simulated from invasive input signals every 60 s. We found that both physiological closed-loop controllers were able to effectively resuscitate subjects out of life-threatening hemorrhagic shock using only intermittent data inputs with a resuscitation effectiveness of at least 95% for each respective controller. We also compared this intermittent signal input to a NIBP cuff and to a deep learning model that predicts blood pressure from a photoplethysmography waveform. Each approach showed evidence of tracking blood pressure, but more effort is needed to refine these non-invasive input approaches. We conclude that resuscitation controllers hold promise to one day be capable of non-invasive sensor input while retaining their effectiveness, expanding their utility for managing patients during mass casualty or battlefield conditions.

Smart Automatic Anesthesia Regulation Dosage

The system uses a MAX30100 sensor to monitor heart rate and oxygen saturation, enabling real-time adjustments to anesthesia delivery via a servo motor connected to a syringe. The motor's movements are calibrated to heart rate data, ensuring precise dosage control. A manual override button allows health care providers to intervene during emergencies. A DHT11 sensor monitors temperature and humidity for environmental awareness, while an LCD displays real-time patient and environmental data. An Arduino Uno processes sensor inputs, controlling servo angles to administer accurate anesthesia volumes based on pre-set thresholds, ensuring patient safety and stability. Index Terms - Syringe pump, Servomotor, Anesthesia regulation, over dose

Adaptive fault-tolerant control for switched nonlinear systems with input deadzone and sensor faults

This research focuses on addressing challenges like input-deadzone, external disturbances, and sensor and actuator faults in switched nonlinear systems with strict-feedback form through the introduction of an adaptive finite-time fault tolerant control (FTC) strategy. To mitigate errors and approximate unknown functions, radial basis function neural networks are leveraged as part of the solution. By combining neural networks with the backstepping technique, an adaptive finite-time tracking controller is formulated. The utilisation of a common Lyapunov function (CLF) for stability analysis ensures that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Moreover, the tracking error is guaranteed to converge within a finite time to a defined compact set. The effectiveness of the proposed control methodology is validated through the successful demonstration in two simulation scenarios, including a practical electromechanical system.

Control of Tollmien–Schlichting waves using particle swarm optimization

The implementation of the Particle Swarm Optimization (PSO) algorithm is investigated to optimize the active attenuation of Tollmien–Schlichting (TS) waves developing in a two-dimensional zero pressure gradient boundary layer. This is done numerically, where the PSO algorithm optimizes the characteristics of harmonic suction and blowing jets, in a feedforward control framework. The PSO-based controller selects and modifies the phase and amplitude of the jets to minimize the pressure fluctuation amplitude downstream of the actuator. To allow for efficient simulation, the 2-dimensional incompressible Navier–Stokes equations are expanded in a harmonic perturbation form and solved in linear and nonlinear variants using harmonic balancing. This study explores the performance of control in both linear and nonlinear development regimes of TS waves through control of single and multi-frequency ensembles of instabilities. Respectively, linear and nonlinear controller design approaches are employed. The findings reveal that the integration of PSO into the control design produces an effective suppression of TS waves through opposition control. The linearly designed controller effectively attenuates single and multi-frequency disturbances. However, when applied in regions of strong nonlinear interactions among instability modes, performance degradation is observed. On the contrary, the nonlinearly designed controller proves effective in mitigating nonlinear multi-frequency instabilities dominating the later stages of growth. A near-complete elimination of TS waves is achieved by accounting for nonlinear interactions among harmonic modes detected by an input sensor. This highlights the benefit of integrating the PSO algorithm in control of TS waves, particularly in the nonlinear growth regime, where classical control methods are generally ineffective.

An automatic 3D tomato plant stemwork phenotyping pipeline at internode level based on tree quantitative structural modelling algorithm

Phenotypic traits of stemwork are important indicators of plant growing status, contributing to multiple research domains including yield estimation, breeding engineering, and disease control. Traditional plant phenotyping with human work faces serious bottlenecks on labour intensity and time consumption. In recent years, the application of Quantitative Structural Modeling (QSM) together with three-dimensional (3D) sensor-based data acquisition techniques provides a feasible solution towards the automatic stemwork phenotyping. Nevertheless, existing QSM-based pipelines are sensitive towards the point cloud quality, and mostly focus on the phenotyping at plant or organ level. Information at internode level which are closely related to photosynthesis and light absorption was generally overlooked. To this end, a 3D automatic stemwork phenotyping pipeline is developed for tomato plants at both plant and internode level. Coloured point clouds are taken as the sensor input of the pipeline. A semantic segmentation based on PointNet++ was used to detect and localise the stemwork points. To improve the quality of the segmented stemwork point clouds, a density-based refining pipeline is proposed containing three main processes: non-replacement resampling, interference branch removal, and noise removal. A Tree Quantitative Structural Modeling (TreeQSM) algorithm was then applied to the stemwork point cloud to construct a digital reconstruction. The target phenotypic traits were finally calculated from the digital model by employing an internode association process. The proposed phenotyping pipeline was evaluated with a test dataset containing three tomato plant cultivars: Merlice, Brioso, and Gardener Delight. The related rooted mean squared errors of calculated internode length, internode diameters, leaf branching angle, leaf phyllotactic angle, and stem length range from 4.8 to 64.4%. Considering the time consuming manual phenotyping process, the proposed work provides a feasible solution towards the high throughput plant phenotyping, from which facilitates the related research on plant breeding and crop management.

End-to-End Autonomous Driving: Challenges and Frontiers.

The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction. End-to-end systems, in comparison to modular pipelines, benefit from joint feature optimization for perception and planning. This field has flourished due to the availability of large-scale datasets, closed-loop evaluation, and the increasing need for autonomous driving algorithms to perform effectively in challenging scenarios. In this survey, we provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. Additionally, we discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework.

Online Calibration of Inertial Sensors Based on Error Backpropagation.

Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems (INSs) allow localization dead reckoning, but they have an integration error that grows over time. Inexpensive inertial measurement units (IMUs) are subject to thermal-dependent error and must be calibrated almost continuously. This article proposes a novel method of online (continuous) calibration of inertial sensors with the aid of the data from the GNSS receiver during the vehicle's route. We performed data fusion using an extended Kalman filter (EKF) and calibrated the input sensors through error backpropagation. The algorithm thus calibrates the INS sensors while the GNSS receiver signal is good, and after a GNSS failure, for example in tunnels, the position is predicted only by low-cost inertial sensors. Such an approach significantly improved the localization precision in comparison with offline calibrated inertial localization with the same sensors.

Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network

The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a multi-objective framework to concurrently address key operational objectives such as minimizing energy consumption, maximizing network lifetime, and enhancing data accuracy and throughput. The mathematical model is formulated to dynamically adapt to changing network conditions and sensor inputs, enabling real-time tuning of fuzzy rules and membership functions through backpropagation neural training. This adaptability ensures optimal performance despite the variable nature of IoT environments. Simulation results demonstrate that the MO-ANFIS model significantly outperforms traditional optimization methods, offering a robust, scalable solution for complex, dynamic WSNs in the IoT landscape. The findings suggest promising applications in various domains, including smart cities, environmental monitoring, and healthcare, where IoT integration is pivotal. This research not only bridges the gap between theoretical fuzzy-neural frameworks and practical IoT applications but also sets a foundation for future explorations into intelligent, adaptive network management systems.

Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes

Generative models, particularly diffusion-based approaches, have gained significant attention recently due to their ability to create realistic outputs. Despite their potential, the application of these models in manufacturing remains largely unexplored. This work presents a framework that addresses this gap by generating machined surface images guided by multiple sensor inputs in manufacturing. The proposed model integrates information from multiple sensors with varying sampling rates using multimodal embedding and employs a latent diffusion model to translate the fused sensor embedding into an image embedding, which is then converted into a machined surface image. The effectiveness of the framework is validated using real-world time-series data, including force, torque, acceleration, and sound, collected from various industrial processes, such as a carbon-fiber-reinforced plastic drilling process. The results demonstrate the model’s ability to predict defects from the generated machined surface images. The proposed approach can potentially revolutionize prognostics and health management (PHM) in smart manufacturing by enabling sensor-guided visual inspection, defect detection, process monitoring, and predictive maintenance.

Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.

To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor's input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor's outputs are PM mass in three size bins, specified as 100-300 nm, 300-600 nm, and 600-1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping machine and a scanning mobility particle sizer, provided the ground truth for size-binned PM mass. A lightweight two-layer feedforward neural network was trained using datasets acquired from a wide range of puffing conditions. The performance of the neural network was tested using unseen data collected using new combinations of puffing conditions. The model-predicted values matched closely with the ground truth, and the accuracy reached 81-87% for PM mass in three size bins. Given the sensor's straightforward optical configuration and the direct collection of signals from undiluted vaping aerosols, the achieved accuracy is notably significant and sufficiently reliable for point-of-interest sensing of vaping aerosols. To the best of our knowledge, this work represents the first instance where machine learning has been applied to directly characterize high-concentration undiluted electronic cigarette aerosols. Our sensor holds great promise in tracking electronic cigarette users' puff topography with quantification of size-binned PM mass, to support long-term personalized health and wellness.

A low-cost prosthetic arm with real-time haptic feedback and gesture control for enhanced user autonomy

In daily life, manipulating and feeling objects is essential for autonomy and engagement in various activities. However, individuals relying on prosthetic limbs often encounter barriers such as high costs, ineffective feedback, and limited design adaptability. Unlike conventional prosthetic limbs, which frequently struggle to integrate seamlessly and provide intuitive control, the robotic arm outlined in this study represents a leap in functionality and user experience. At the core of its design lies a specialized glove outfitted with a calibrated array of flex-sensing resistors capable of precisely detecting and interpreting the wearer's movements. These sensors input to the robotic arm, translating the user's gestures into motions that follow human hands. The fingertips are equipped with force-sensing resistors to discern the magnitude of force applied during gripping actions. This real-time sensory feedback is conveyed to the user through a haptic system integrated into the glove. By modulating the intensity and pattern of vibrations, users can gauge their grip strength and manipulate their grasp of the object accordingly. Comprehensive experiments were conducted across various scenarios to assess its performance, agility, strength, and sensory accuracy. Through refinement, the author aims to optimize functionality and reliability, enhancing practicality for users. This project has broader implications for the prosthetics community. By utilizing the scalability and cost-effectiveness of this model, the author envisions tailored solutions that empower individuals with limb differences to live more independently.

Investigation on Effect of Nonlinear Parameters in torque accuracy of Permanent Magnet Synchronous Machines

EVs and PHEVs are mainly driven by inverter control topologies which will be driving permanent magnet synchronous machines. PMSM is widely used for high drive performance. However, due to parameter variations high torque accuracy is not achieved. The performance of the PMSM drives is widely affected which is caused mainly due to environmental factors and aging. This would include component tolerance, input signal tolerance, machine parameters, software algorithms, sensor accuracy and calibration. It is important to understand these factors and make the tuning throughout the lifecycle since it may show performance deviations over the time and may lead to field complaints. As a step-by-step procedure, the factors which affects the torque accuracy are identified as part of the literature review. This is given as input to a cause effect relationship model which could demonstrate the deviation points. Simulation is performed by varying the operating points at different regions in the e-drive system modeled in Matlab/Simulink. Simulation results are used with different operating points to find out the influential parameters contributing to the deviation. The outcomes derived from the simulations are analyzed and dominant factors are determined. It is observed that this behavior will vary according to the changes in speed and torque. Correlation coefficient is derived from the statistical analysis. The future scope will be applying the optimization techniques and verification of improvements.

AI for Traffic Prediction and Management

Urban traffic congestion is a growing problem in cities across the globe, contributing to long delays, higher fuel consumption, environmental degradation, and economic losses. Conventional traffic management systems often depend on static data and rule-based approaches, which fall short in dealing with the complexity and variability of modern traffic. This paper introduces an AI-based traffic management approach that utilizes machine learning models to provide real-time traffic forecasts. By integrating historical data, live sensor inputs, and machine learning techniques, this system aims to enhance traffic flow, alleviate congestion, and improve travel efficiency. The model is compared against existing systems, demonstrating improved accuracy, flexibility, and scalability. Results indicate that the AI-based system offers significant advantages in managing urban traffic, surpassing traditional methods. The study further elaborates on how AI-powered traffic management can cut travel times by ensuring efficiency and safety, therefore playing a part in environmental sustainability through emission reduction, and the opportunities and challenges it brings to the table in the implementation of AI in traffic systems.

DEVELOPMENT AND EVALUATION OF A MULTI-CHAMBER ENVIRONMENTAL CONTROL SYSTEM

Growth chambers are control devices used in plant research. However, their high costs limit their application to a single environment, which compromises their efficiency for the generation of experimental data. The objective of this research was to design, construct, and assess the performance of a multi-chamber environmental control system capable of regulating both air temperature (AT) and relative humidity (RH). The evaluation was carried out using Syngonium podophyllum Schott plants. Three constant target temperature values (18, 23, and 40 °C) were established, together with a target RH value of 60 %. AT and RH data were recorded every 4 min for three days. The system was built with 15 growth chambers and the following devices: output (AT, RH, and lighting control), input (sensors), and central control managed by two Arduino® Mega 2560 based microcontrollers, plus a programmable logic controller. Programming was done in open-source languages such as Arduino® and Python®. Results indicate that the absolute error (AE) and standard deviation (SD) in AT were higher when the target temperature was lower (AE = 0.59 °C and SD = 0.93 °C) compared to the other higher target temperatures (AE = 0.31 °C and SD = 0.43 °C at 23 °C, and AE = 0.25 °C and SD = 0.51 °C at 40 °C). On the other hand, the AE in RH increased as temperature increased, going from AE = 3.75 % and SD = 5.12 % at 18 °C, to AE = 9.23 % and SD = 10.77 % at 40 °C. The use of open-source technologies allowed the construction of a multi-chamber environmental control system capable of replicating controlled environments.

Fixed-time adaptive control for nonstrict-feedback nonlinear systems with sensor faults and input saturation

This paper addresses the challenge of fixed-time adaptive control for nonstrict-feedback nonlinear systems with input saturation and sensor faults. The difficulty arising from the non-smooth saturation nonlinearity is to overcome by employing a smooth non-affine function to estimate the saturation signal. For systems with unknown functions, radial basis function neural networks (RBFNN) are utilized to approximate these unknown nonlinearities. Using the backstepping technique, an adaptive fixed-time control (FxTC) strategy is developed based on the Lyapunov function method. This approach ensures that all signals within the closed-loop system remain bounded, and the system output tracks the desired signal to a bounded compact set within a fixed time. Moreover, the convergence time of the tracking error is independent of the system’s initial states. The proposed approach is validated using a pendulum system and one-link manipulator system as examples. Additionally, comparative results are presented in the simulation section to demonstrate the superiority of the proposed method.

Clinical Applications and Future Translation of Somatosensory Neuroprostheses.

Somatosensory neuroprostheses restore, replace, or enhance tactile and proprioceptive feedback for people with sensory impairments due to neurological disorders or injury. Somatosensory neuroprostheses typically couple sensor inputs from a wearable device, prosthesis, robotic device, or virtual reality system with electrical stimulation applied to the somatosensory nervous system via noninvasive or implanted interfaces. While prior research has mainly focused on technology development and proof-of-concept studies, recent acceleration of clinical studies in this area demonstrates the translational potential of somatosensory neuroprosthetic systems. In this review, we provide an overview of neurostimulation approaches currently undergoing human testing and summarize recent clinical findings on the perceptual, functional, and psychological impact of somatosensory neuroprostheses. We also cover current work toward the development of advanced stimulation paradigms to produce more natural and informative sensory feedback. Finally, we provide our perspective on the remaining challenges that need to be addressed prior to translation of somatosensory neuroprostheses.

Enhancing Situational Awareness for Remote Control of Ship Cranes

Abstract Cargo handling in ship operations poses risks due to heavy loads, necessitating innovation to mitigate accidents. Automation and remote control offers safer and more efficient operations by moving people from hazardous situations. Smaller and remote ports often rely on geared ships, which are typically manually operated. A first step towards automation is to remotely control these cranes. In remote control, the operator needs to gain situational awareness through sensor inputs. Previous research has shown that there is a need to investigate the sensor configuration and placement to provide situational awareness, particularly considering the placement of cargo onboard. This study seeks to identify needed sensor configurations for situational awareness in remote control of ship cranes using a simulator framework. A modular crane simulator framework has been developed to evaluate situational awareness for different sensor configurations. The sensor configuration has been evaluated with feedback from a crane expert. This research has identified potential sensor configurations for enhancing situational awareness in remote crane operations. The developed simulator framework has demonstrated its flexibility and efficiency as a platform for testing and optimizing cargo handling operations.

Аналіз даних теплового режиму комутаційного обладнання комп’ютерних мереж на основі відновлення сигналів температурних датчиків

The article considers the task of analyzing distributed data of temperature modes of chips of switching equipment of computer networks. For this, a temperature measurement system using a temperature sensor is used. In the case when the temperature sensor is inside the chip, the speed of the measurement system's response to temperature changes is satisfactory. However, when the sensor is outside the chip, due to the inertness of the thermal contact, the response speed of the measuring system is low. In this situation, the effectiveness of the control system becomes unsatisfactory. To overcome the inertness of temperature sensors, it is proposed to analyze the distributed data that is received in digital form on the data processing server by restoring the distorted signals of nonlinear measuring subsystems «chip – temperature sensor» based on the use of their mathematical models in the form of a partial sum of the Volterra integro-power series. The identification of the mathematical model of the measurement subsystem is carried out on a finite interval of time by conducting a series of experiments using test signals. The method of reducing the number of test signals based on taking into account the specificity of the impact of nonlinearity on the results of experiments is considered. The obtained model is the basis for solving the inverse problem of restoring the signal of temperature influence at the sensor input. Since this problem is incorrect, it is suggested to supplement the model with a regularization parameter and reduce the problem to a correct one. To use the model over an infinite period of time, a computer modeling technique is proposed using restarts of computing processes, which are carried out in several streams with a shift in time. The result of calculations is formed by combining fragments from different streams. To check the reliability of the results obtained by applying the developed method, the solutions of the model problems are given.