Smart Sensor Systems

A low-cost smart measurement system for resistive (bridge) and capacitive sensors is presented and demonstrated. The measurement system consists of three main parts: the sensor element, a universal transducer interface (UTI) and a microcontroller. The UTI is a sensor-signal-to-time converter, based on a period-modulated oscillator, which is equipped with front-ends for many types of resistive (bridge) and capacitive sensors, and which generates a microcontroller-compatible output signal. The microcontroller performs data acquisition of the output signals from the interface UTI, controls the working status of the UTI for a specified application and communicates with a personal computer. Continuous auto-calibration of the offset and the gain of the complete system is applied to eliminate many nonidealities. Experimental results show that the accuracy and resolution are 14 bits and 16 bits, respectively, for a measurement time of about 100 ms.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The U.S. Army has a critical need for the capability provided by a multifunction sensor. This is (in effect) a smart sensor system that can adapt to environmental conditions and adjust its mode of operation to effectively counter any threat it meets. It will have an intelligent signal processor which has all of the system's sensor signals to choose from. The processor chooses the appropriate signal information to rapidly detect, acquire, track, and automatically identify all targets in the vicinity of the sensor under a wide variety of battlefield scenarios and environmental conditions. The multiphenomenology signal information provides the flexibility to overcome the adverse effects of clutter, countermeasures (both active and passive), illumination, obscurants, target orientation, and weather. It should be noted, however, that the types of sensory information required is dependent on the mission and the operating environment. For instance, a strategic defense sensor operating in space can use (and will need) different types of sensor data than the multifunction sensor employed on an attack helicopter. In fact, the sensor configuration on a helicopter operating in Saudi Arabia may be quite different from one that is deployed to Vietnam. For the purpose of this paper we generalize about the technologies desired for an adaptable, `smart' sensor system. We do not specify a particular mission nor define a specific threat. However, in any case, we can assume the need to fuse sensor signal information in an intelligent processor to provide robust performance in the battlefield environment. 12© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An emerging area in ubiquitous computing is networked sensor systems. The typical approach is to connect sensor-actuator devices using classic network infrastructures at a low level. These networks can serve as infrastructures to dynamically integrate, sensors and actuators into complex interactive systems while providing convenient services and interfaces to users. A prominent scenario for ubiquitous computing and ad hoc networking is an in-house environment using smart sensor system. Shaman, an extendable Java-based service gateway for networked sensor systems, integrates small network-attached sensor-actuator modules (SAMs) into heterogeneous, high-level networking communities. The system unburdens its connected SAMs by transferring functionality from the SAMs to the gateway.

While our knowledge of tropical montane forests (TMFs) is limited, human activities continue to damage them. Remote sensing has shown its usefulness for forest investigations with difficult terrain. This study systematically reviewed the applications of spaceborne remote sensing to TMFs with regards to time, spatial distribution, journal publication, methodology, theme, sensor systems, vegetation zones, research needs, and techniques applied. Our review found that the number of papers published between 1997 to 2021 has increased considerably. Experts used optical sensors with low to medium spatial resolution (85.76%), and that the use of synthetic aperture radar received little attention (12.70%). Published articles varied substantially between continents, with the Americas having the most studies (62.26%), leaving Asia, Africa, and Oceania behind. Most research has focused on forestry (42.66%), climate science (11.01%), and disaster management (9.63%). This paper highlighted the challenges and opportunities in the application of remote sensing in TMF conservation and management.

<div class="htmlview paragraph">This paper reviews new engine control technologies and future trends, particularly in mechanical and electrical engine control components, the application of modern control theory, and new advances in sensor technologies.</div> <div class="htmlview paragraph">A system which utilizes engine cylinder combustion information is representative of these new trends. Like other such sensor systems, the combustion sensor still has problems which must be overcome before practical application is possible.</div> <div class="htmlview paragraph">The factors and problems involved in developing this sensor will be discussed, along with how the relationship between sensing information and engine performance can be used to improve engine performance.</div>

With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.

Simple, accurate, portable, and selective gas sensors with autonomous, remote, and real-time access have become a requisite in various fields of applications. In this article, we report the development of a stand-alone and selective gas sensor system incorporating a single resistive sensor with wireless monitoring and Internet connectivity. The sensor is fabricated in-house with platinum-decorated tin-oxide hollow spheres as the sensing material, which exhibits a prominent response toward the tested volatile organic compounds (VOCs) at different concentrations. The intelligence in terms of accurate identification of VOCs and their concentration is attained by employing a machine learning tool based on a deep neural network. The applied model displays an average accuracy of 96.43% with a fast prediction speed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$310~\mu \text{s}$ </tex-math></inline-formula> , allowing a real-time recognition capability. The wireless connectivity is established utilizing a low-power microcontroller board and a Bluetooth module. The real-time data is made available for the users over an Android-based mobile application and a webpage while utilizing cloud services through the Internet. The implemented system is successfully experimented with and validated under different test conditions that verify the whole platform. Further, the sensor system can be potentially applied to a remote application without needing any manual involvement. The demonstrated work with an Internet of Things (IoT) paradigm strengthens the next-generation gas sensing technology for developing smart, selective, and real-time gas sensor systems.

Advanced analytical applications require smart materials and sensor systems that are able to adapt or be configured to specific tasks. Based on reversible photochemistry in nanoporous materials, we present a sensor array with a selectivity that is reversibly controlled by light irradiation. The active material of the sensor array, or electronic nose (e-nose), is based on metal–organic frameworks (MOFs) with photoresponsive fluorinated azobenzene groups that can be optically switched between their trans and cis state. By irradiation with light of different wavelengths, the trans–cis ratio can be modulated. Here we use four trans–cis values as defined states and employ a four-channel quartz-crystal microbalance for gravimetrically monitoring the molecular uptake by the MOF films. We apply the photoprogrammable e-nose to the sensing of different volatile organic compounds (VOCs) and analyze the sensor array data with simple machine-learning algorithms. When the sensor array is in a state with all sensors either in the same trans- or cis-rich state, cross-sensitivity between the analytes occurs and the classification accuracy is not ideal. Remarkably, the VOC molecules between which the sensor array shows cross-sensitivity vary by switching the entire sensor array from trans to cis. By selectively programming the e-nose with light of different colors, each sensor exhibits a different isomer ratio and thus a different VOC affinity, based on the polarity difference between the trans- and cis-azobenzenes. In such photoprogrammed state, the cross-sensitivity is reduced and the selectivity is enhanced, so that the e-nose can perfectly identify the tested VOCs. This work demonstrates for the first time the potential of photoswitchable and thus optically configurable materials as active sensing material in an e-nose for intelligent molecular sensing. The concept is not limited to QCM-based azobenzene-MOF sensors and can also be applied to diverse sensing materials and photoswitches.

Smart MEMS and Sensor Systems

Smart sensor systems play a decisive role in the condition assessment of concrete sewer pipes going through microbial corrosion. Few Australian water utilities adopt a predictive analytic model for estimating the corrosion. They require sensor inputs like sewer air temperature data for corrosion prediction. A sensor system was developed to monitor the daily variation of sewer air temperature inside the harsh sewer environmental conditions. However, a diagnostic tool to evaluate the streaming sensor data is vital for reliable monitoring. In this context, this paper proposes a temporal forecasting driven approach for anomaly detection in sewer air temperature sensor system. Several temporal forecasting models were comprehensively evaluated and adopted Facebook's Prophet method based forecasting to develop an anomaly detection approach. The proposed approach was evaluated with sewer air temperature sensor data and the results indicate a reasonable anomaly detection performance.

The generic term ldquosmart sensorrdquo addresses several aspects including miniaturization, sensor communication and flexible integration in higher-level systems. It involves the use of signal processing able to eliminate influencing effects and extract high quality sensory information in order to realize a robust signal transmission. In this contribution, we will give an overview about the history of the term of ldquosmart sensorsrdquo since its release in the 70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and categorize the main aspects involved with this term as a basis for the prognosis of future trends. In the second part of the paper we provide some up-to-date examples and future trends for important developments in this field including ldquoenergy harvestingrdquo, ldquovaried input sensorsrdquo and novel sensors based on nanotechnology.

Context: The speed of technological advancement has raised concerns about the impact of automation on work. Previous studies have analyzed this impact on the private sector, but there is little research focused on the Brazilian public sector, which faces additional challenges in dealing with this issue. Objective: To estimate the impact of automation on intermediate-level federal public positions and indicate professional retraining paths for civil servants, in the face of technological changes. Methodology: A 4-step approach was used, involving (1) mapping of automation technologies, (2) extraction and review of the job duties of 142 intermediate-level public positions from a government document with the support of AI models, (3) assessment of the importance level and horizon of automation of each job duty by GPT-4 Turbo and (4) suggestion of professional retraining courses for each position. Results: A considerable potential for automation of the analysed positions was identified, with 72% of the job duties with different automation levels in an immediate or short-term impact horizon. However, also considering the frequency and importance of each job duty, a considerable part of the positions had an impact between 0.10-0.28 points on a scale of 0 to 1. The technologies with the most occurrences in the analysis of automation impact were Machine Learning, Smart Sensors and Process Digitization Systems. In the end, 236 professional retraining courses were suggested to better prepare employees for possible technological impacts.

A high-accuracy Brillouin frequency shift (BFS) measurement system for vector Brillouin optical time-domain analysis-based temperature sensor is proposed, in which double sideband modulation is used and the stimulated Brillouin scattering (SBS) gain and loss processes work together to generate a superimposed SBS phase-shift spectrum. The measurement principle is analyzed by mathematical modeling and the proof-of-concept experiment is performed by using a 100-m long standard single-mode fiber. The theoretical and experimental results reveal that the temperature sensitivity of BFS obtained from the measured SBS phase-shift spectrum is 1.059 MHz / ° C , and the measurement error of temperature is only half that in traditional single sideband-based system, which indicates that the proposed technique can realize high-accuracy temperature measurement and have huge potential in the field of long-distance and high-accuracy sensing.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

The 2nd International Conference on Mechatronics and Smart Systems (CONF-MSS 2024) is an annual conference focusing on research areas including mechanics and automation, robotics, electronics and sensors, smart system. It aims to establish a broad and interdisciplinary platform for experts, researchers, and students worldwide to present, exchange, and discuss the latest advance and development in mechanics, electronics, and smart system. This volume contains the papers of the 2nd International Conference on Mechatronics and Smart Systems (CONF-MSS 2024). Each of these papers has gained a comprehensive review by the editorial team and professional reviewers. Each paper has been examined and evaluated for its theme, structure, method, content, language, and format. Cooperating with prestigious universities, CONF-MSS 2024 organized four workshops in Daytona Beach, Abu Dhabi, and Beijing. Dr. Marwan Omar chaired the workshop “Urban Evolution: Advancing Smart Transportation in the Age of Intelligent Cities”, which was held at Embry-Riddle Aeronautical University. Dr. Yazeed Ghadi chaired the workshop “Enhancing Human-Machine Interaction through Multimodal Interfaces: A User-Centric Approach” at Al Ain University. Dr. Ziyi Yang chaired the workshop “Space-Air Cross-Domain Collaboration Communication” at Beijing Institute of Technology. And Dr. Xinqing Xiao chaired the workshop “Mechatronics and Smart Sensing Systems” at China Agricultural University. Besides these workshops, CONF-MSS 2024 also held an online session. Eminent professors from top universities worldwide were invited to deliver keynote speeches in this online session, including Dr. Marwan Omar from Illinois Institute of Technology, Dr. Yazeed Ghadi from Al Ain University, and Dr. Xinqing Xiao from China Agricultural University. They have given keynote speeches on related topics of mechanics, electronics, and smart system. On behalf of the committee, we would like to give sincere gratitude to all authors and speakers who have made their contributions to CONF-MSS 2024, editors and reviewers who have guaranteed the quality of papers with their expertise, and the committee members who have devoted themselves to the success of CONF-MSS 2024. Dr. Marwan Omar General Chair of Conference Committee List of Committee Members are available in this Pdf.