Integration Of Sensors Research Articles

Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods

The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.

BEATRIX: An open source humanoid head platform for robotics teaching and research

This paper introduces BEATRIX, a novel robotic head designed to bridge the gap between theoretical knowledge and practical experience in the field of robotics at universities. The BEATRIX robot comprises a head actuated by a neck-like mechanism with three stepper motors, two cameras and two microphones for acquisition of visual and audio information from the environment. The robot can be connected to any external computer for the design and implementation of algorithms for applications in human–robot interaction. The proposed robotic platform has been used successfully with undergraduate and master students implementing tasks such as face detection and tracking, sound detection and tracking, robot control and graphical user interfaces. This paper includes lists of all the robot components, assembly instructions, and links to all CAD and software files, facilitating replication and further exploration. The robot design and integration of visual and audio sensors enables the development of engaging educational tutorials and robot experiments, enhancing the teaching and learning experience in robotics.

Multimodal Human–Robot Interaction Using Gestures and Speech: A Case Study for Printed Circuit Board Manufacturing

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments.

Intelligent Vehicle Accident Detection System

Road traffic accidents in Nigeria represent a significant public safety concern, with thousands of lives lost annually. The Intelligent Vehicle Accident Detection System (IVADS) addresses this issue by providing a system that detects accidents automatically and instantly notifies emergency responders with real-time location information. IVADS integrates multiple sensors, GPS, and GSM technologies to achieve accurate accident detection and rapid communication. This paper provides a comprehensive review of the system’s design, implementation, testing results, and potential implications for road safety improvements in Nigeria. This work builds upon existing research on accident detection, using innovative sensor integration and machine learning for precise detection.

Outdoor mesoscale fabricated ecosystems: Rationale, design, and application to evapotranspiration

The disparity in scale, complexity, and control level between laboratory experiments and field observational studies has shaped both the methodologies employed and the nature of the research questions pursued in ecology and hydrology. While lysimeters and fabricated ecosystems suitably fit in this gap, their use as mesoscale experimental facilities has not been fully explored because of the limited manipulating capabilities and integration with imaging and monitoring methods, particularly for soil functioning. The proposed fabricated ecosystem (4.7 L × 1.2 W × 1.2 H m) focuses on the spatiotemporal integration of point sensors and imaging methods along the soil-plant-atmosphere continuum. Because energy and water fluxes are key environmental drivers, the designed setup was first applied to a multi-approach evapotranspiration investigation. Below the ground, electrical resistivity tomography (ERT) was combined with soil water sensors and a distributed temperature profiling system. Together, they provided the 3D monitoring of water and temperature changes, and thus an estimation of the evapotranspiration, as well as the interpretation of its below-ground controlling processes. Above-ground sensors supported a classical energy balance investigation that was compared with the lysimeter load changes and the ERT-based ET estimation. Our results provide first experimental evidence of water and temperature spatiotemporal variability at the lysimeter scale, and thus explain the discrepancies among the three estimated evapotranspiration time series and their seasonality. Beyond evapotranspiration, the multi-approach investigation of water and energy fluxes emphasizes how mesoscale setups can further support the development and upscaling of methods and models, as well as their integration and application under expected climate disturbances.

Enhancing Industrial Automation Through AI-driven Sensors: A Comprehensive Study on Efficiency, Safety, and Predictive Maintenance

The fast development of the industry of Industry 4.0 is reshaping the manufacturing landscape. The key to this transformation is the integration of sensors with artificial intelligence, making traditional sensors capable of processing data and making decisions. This paper will concentrate on how AI-driven smart sensors-such as temperature, pressure, infrared, and optical sensors are redefining industrial automation. Followed by case studies across sectors like manufacturing and energy demonstrate how sensors reduce downtime, optimize performance, and anticipate failures.

Design Research on Smart Wearable Devices: A Case Study of Apple Watch

Abstract. Smart wearable devices have rapidly evolved to become essential components of modern technology, transforming the way users interact with the digital world. This paper explores the design and development process of smart wearable devices, focusing on the Apple Watch as a case study. Key design principles such as sensor integration, power management, user interface, and connectivity are discussed in detail. Apple Watch, as one of the leading devices in this field, is examined through its hardware architecture, software environment, and communication protocols. Through this research, we delve into the technical challenges faced during its development and the solutions that make the Apple Watch a successful wearable product. Additionally, the paper highlights future trends in wearable device design, particularly in terms of energy efficiency and enhanced human-computer interaction. The findings indicate that the success of wearable devices, such as the Apple Watch, hinges on achieving a fine balance between performance, user comfort, and energy efficiency, while continuously innovating in the areas of sensor technology and machine learning integration. Ultimately, this study contributes to the understanding of how modern wearable devices are designed and how they can be further improved for future applications in health monitoring, entertainment, and productivity enhancement.

Wearable Robotic Exoskeletons for Assisted Mobility: Enhancing Rehabilitation and Quality of Life for Neuromuscular Disorders

Wearable robotic exoskeletons represent a significant advancement in assistive technology, offering enhanced mobility and improved quality of life for individuals with neuromuscular disorders. This research explores the integration of robotics and biomechanical engineering to develop exoskeleton systems tailored for rehabilitation and daily mobility assistance. Key areas of investigation include the use of sensor-driven actuators for real-time movement adaptation, the role of artificial intelligence (AI) in customizing therapy sessions, and the impact of these devices on neuroplasticity during rehabilitation. By analyzing real-world applications in conditions such as spinal cord injuries, muscular dystrophy, and stroke recovery, the study evaluates the effectiveness of exoskeletons in restoring functional movement and independence. Ethical considerations, including accessibility, cost, and long-term usability, are also addressed. This research underscores the transformative potential of wearable robotic exoskeletons, emphasizing their role in bridging the gap between assistive mobility and therapeutic rehabilitation. With the integration of AI and advanced sensors, these systems hold promise for reshaping rehabilitation paradigms, improving patient outcomes, and enhancing the overall quality of life for individuals with neuromuscular impairments.

Intelligent mobility: Unveiling triboelectric nanogenerator-powered intelligent tire realization

Energy harvesting and sensing devices utilizing triboelectric nanogenerators (TENG) are advancing rapidly and offer significant potential for a wide range of future applications. Simultaneously, the integration of sensors into intelligent tires enables precise estimation of tire conditions, thereby enhancing vehicle intelligence. As the concept of intelligent tires has long intrigued researchers, the pursuit of a cost-effective, self-powered, and easily integrated sensor remained unexplored until the emergence of TENGs. The main research objectives of this paper are: (1) to test and optimize the structure of TENG and design a sandwich-type TENG (St-TENG), which can be used as a tire sensor; (2) to integrate St-TENG into the tire, establish the mapping relationship between St-TENG signal and tire load, and achieve effective estimation of tire load. The St-TENG was successfully prepared using several easily available and inexpensive materials, and it has robust performance in the confined and harsh environment inside the tire. The methodology proposed herein charts a novel path for TENG and intelligent tire development, paving the way for the evolution of intelligent tire systems in the foreseeable future.

Modular Reconfigurable Approach Toward Noninvasive Wearable Body Net for Monitoring Sweat and Physiological Signals.

In the realm of wearable technology, strategically placing sensors at various body locations enhances the detection of diverse physiological indicators crucial for remote medical care. However, current devices often focus on a single body part for specific physical parameters, which hinders the seamless integration of sensors across multiple body parts and necessitates redesign for new detection capabilities. Here, we propose a modular, reconfigurable circuit assembly method that can be adaptable for multiple body locations to construct the body net. By simply reassembling different child modules with the base module using flexible printed circuit board connectors, we can efficiently detect various parameters including sweat ion indicators, electrocardiogram signals, electromyography signals, motion data, heart rate, blood oxygen saturation, and skin temperature. These data can be transmitted to a mobile phone app via a Bluetooth Low Energy protocol for further evaluation. Comparative evaluations against established commercial devices substantiate the viability of our sensor technology. In addition, results from wearable body network detections using reconfigurable sensors across multiple body parts of volunteers also indicate promising application prospects, demonstrating the extensive potential for regular health monitoring and clinical applications.

Improved human identification by multi-biometric image sensor integration with a deep learning approach

Improved human identification by multi-biometric image sensor integration with a deep learning approach

Integration of Gas and Ultrasonic Sensors for Monitoring Air Quality and Smart Waste Bin levels determination: An IOT illustration in Solid waste management

Integration of Gas and Ultrasonic Sensors for Monitoring Air Quality and Smart Waste Bin levels determination: An IOT illustration in Solid waste management

Rancang Bangun Sistem Kontrol Irigasi Otomatis Berbasis IoT untuk Tanaman Stevia

This research focuses on developing an IoT-based automatic irrigation control system for stevia plants to optimize plant growth and water usage efficiency. The system integrates ESP32 microcontroller with soil moisture sensors, DS18B20 temperature sensors, and DHT11 environmental sensors for comprehensive monitoring. Using Research and Development (R&D) methodology with an experimental approach, the system was designed and implemented to automatically control irrigation based on soil moisture levels. The results demonstrate that the system successfully maintains optimal soil moisture by activating the pump when moisture levels fall below 38% and deactivating it above 40%. Real-time monitoring through the Blynk platform enables remote observation and control of environmental parameters. The integration of multiple sensors with IoT technology provides an efficient solution for stevia plant irrigation management, offering potential applications in smart agriculture.

Assessing Changes in Motor Function and Mobility in Individuals with Parkinson's Disease After 12 Sessions of Patient-Specific Adaptive Dynamic Cycling.

This pilot randomized controlled trial evaluated the effects of 12 sessions of patient-specific adaptive dynamic cycling (PSADC) versus non-adaptive cycling (NA) on motor function and mobility in individuals with Parkinson's disease (PD), using inertial measurement unit (IMU) sensors for objective assessment. Twenty-three participants with PD (13 in the PSADC group and 10 in the NA group) completed the study over a 4-week period. Motor function was measured using the Kinesia™ sensors and the MDS-UPDRS Motor III, while mobility was assessed with the TUG test using OPAL IMU sensors. The PSADC group showed significant improvements in MDS-UPDRS Motor III scores (t = 5.165, p < 0.001) and dopamine-sensitive symptoms (t = 4.629, p = 0.001), whereas the NA group did not improve. Both groups showed non-significant improvements in TUG time. IMU sensors provided continuous, quantitative, and unbiased measurements of motor function and mobility, offering a more precise and objective tracking of improvements over time. PSADC demonstrated enhanced treatment effects on PD motor function compared to NA while also reducing variability in individual responses. The integration of IMU sensors was essential for precise monitoring, supporting the potential of a data-driven, individualized exercise approach to optimize treatment outcomes for individuals with PD.

Hydrogel-Forming Microneedles and Applications in Interstitial Fluid Diagnostic Devices.

Hydrogel-forming microneedles are constructed from or coated with polymeric, hydrophilic materials that swell upon insertion into the skin. Designed to dissolve or disintegrate postinsertion, these microneedles can deliver drugs, vaccines, or other therapeutics. Recent advancements have broadened their application scope to include the collection, transport, and extraction of dermal interstitial fluid (ISF) for medical diagnostics. This review presents a brief introduction to the characteristics of dermal ISF, methods for extraction and sampling, and critical assessment of the state-of-the-art in hydrogel-forming microneedles for ISF diagnostics. Key factors are evaluated including material composition, swelling behavior, biocompatibility, and mechanical strength necessary for effective microneedle performance and ISF collection. The review also discusses successful examples of dermal ISF assays and microneedle sensor integrations, highlighting notable achievements, identifying research opportunities, and addressing challenges with potential solutions. Despite the predominance of synthetic hydrogels in reported hydrogel-forming microneedle technologies due to their favorable swelling and gelation properties, there is a significant variety of biopolymers and composites reported in the literature. The field lacks consensus on the optimal material, composition, or fabrication methods, though emerging evidence suggests that processing and fabrication techniques are critical to the performance and utility of hydrogel-forming microneedles for ISF diagnostics.

Hole-assisted three-core fiber directional coupler operated in reflection mode for vector bending measurement

We demonstrate a vector bending sensor operated in reflection mode based on a hole-assisted three-core fiber (HATCF) coupler. The sensor is built by splicing a piece of HATCF with a gold film-coated end to a single-mode fiber (SMF). The gold film on the center core end is etched with femtosecond laser and only the lights transmitted in the two suspended cores of the HATCF are reflected. The center core couples with the two suspended cores at different wavelengths, respectively. The bending direction and curvature are identified by monitoring the wavelength of the two coupled peaks. The maximum curvature sensitivity of the sensor is 15.146 nm/m−1. The temperature sensitivity is less than −58.8 pm/°C. The vector bending sensor operated in reflection mode has the advantages of a compact structure and simple transmission line, which is conducive to the miniaturization and integration of optical fiber bending sensors.

Exploring the Role of Transformer Models in Vision, Multi-Modal, and Orbital Data for Autonomous Driving

Abstract. Autonomous driving aims to reduce human error in driving, improve traffic efficiency, and provide a more comfortable driving experience. The integration of computer vision, advanced sensors, and machine learning has been pivotal in this advancement. The introduction of Transformer models has particularly revolutionized the field by offering a novel approach to processing data through attention mechanisms, which is crucial for tasks involving complex relationships between data elements. The paper categorizes research into three main approaches based on input data types: camera-based perception, multi-modal data fusion, and orbital data integration. As autonomous driving technology progresses towards higher levels of autonomy, with L2+ systems becoming standard, challenges remain in accurately interpreting complex environments, handling edge cases, and navigating legal and regulatory landscapes. The paper concludes that while Artificial Intelligence (AI) and deep learning advancements have brought autonomous driving closer to full realization, further research is necessary to address current limitations and ensure safe and reliable autonomous vehicle operation.

Exploring the Role of Transformer Models in Vision, Multi-Modal, and Orbital Data for Autonomous Driving

Abstract. Autonomous driving aims to reduce human error in driving, improve traffic efficiency, and provide a more comfortable driving experience. The integration of computer vision, advanced sensors, and machine learning has been pivotal in this advancement. The introduction of Transformer models has particularly revolutionized the field by offering a novel approach to processing data through attention mechanisms, which is crucial for tasks involving complex relationships between data elements. The paper categorizes research into three main approaches based on input data types: camera-based perception, multi-modal data fusion, and orbital data integration. As autonomous driving technology progresses towards higher levels of autonomy, with L2+ systems becoming standard, challenges remain in accurately interpreting complex environments, handling edge cases, and navigating legal and regulatory landscapes. The paper concludes that while Artificial Intelligence (AI) and deep learning advancements have brought autonomous driving closer to full realization, further research is necessary to address current limitations and ensure safe and reliable autonomous vehicle operation.

Parking Assistant with the Simulation of Application

Smart Parking can be used for any city. It aims to improve the efficiency and sustainability of urban transportation systems by using technology to manage parking systems. This article provides an overview of the different components of smart parking systems. The increasing vehicular population in urban areas has led to significant challenges related to parking management, exacerbating traffic congestion, environmental impact, and user dissatisfaction. The app facilitates real- time information on available parking spaces, reservation capabilities, and payment integration, thereby enhancing user experience and operational efficiency. We summarize the methodologies applied in developing the app, which include sensor integration for dynamic parking space monitoring and mobile application development for user interaction. The article concludes with a consideration of the future of smart parking and its role in the overall development of smart cities. In conclusion, the findings serve as a basis for future advancements in intelligent parking systems, ensuring improved service delivery for urban commuters.

Optimasi Jaringan Sensor Nirkabel untuk Monitoring Suhu dan Kelembaban

The development of Internet of Things (IoT) technology has provided new opportunities to improve monitoring through the integration of temperature and humidity sensors in wireless networks. This study aims to optimize a wireless sensor network for temperature and humidity monitoring. This study combines an experimental approach with network performance analysis and optimization methods. First, the selection of temperature and humidity sensors is carried out. Next, an efficient and reliable wireless sensor network architecture is designed to collect temperature and humidity data from various locations. The process of sending data from sensors to base stations and integration with IoT platforms are also studied. Furthermore, a series of experiments are conducted to test the performance of the wireless sensor network under real conditions. Analysis of the collected data is used to identify potential improvements in network performance and improve system reliability and responsiveness. The performance parameters evaluated include connection stability, data throughput, energy consumption, and communication latency. The results of this study contribute to the development of a monitoring system that is reliable, efficient, and easy to implement.