Sensor Suite Research Articles

CALIBRATION TOOL FOR TILT SENSORS FOR CHECKING THE INSTABILITY OF MOTOR VEHICLES

The article deals with the development and determination of the uncertainty of a calibration tool for tilt sensors for the control of motor vehicle instability. The stability of motor vehicles is critical, especially when driving on inclined terrain, when instability and overturning of the vehicle can occur. For this purpose, it is necessary to install and calibrate a suitable tilt sensor.

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM2.5), a significant portion of ambient PM2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM2.5, are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM2.5 both temporally and spatially. Monthly network-average BC was 3 × higher in winter than late spring, while PM2.5 was only 10% lower. In both seasons, average PM2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM2.5, and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM2.5. Thus, including BC in addition to PM2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

Multi-Objective Robust Trajectory Design for Powered Descent and Landing

A robust trajectory optimization approach for guidance algorithm gain and target vector selection for powered descent and landing is developed. A genetic algorithm is used to determine optimized guidance algorithm parameters to minimize the impact of initial condition, environment, navigation, and vehicle property uncertainty on flight performance for a given sensor suite. Vehicle state uncertainties are computed efficiently using linear covariance analysis techniques. When implemented in the guidance algorithm, the optimized gains and target vectors shape a trajectory that has more favorable conditions for a given navigation sensor suite, resulting in improved flight performance. As a demonstration of this method, the optimized guidance parameters are found for a multiphase trajectory from powered descent initiation to touchdown for a robotic lunar landing mission. Single-objective optimization results demonstrate a reduction in uncertainty and an improvement in nominal performance. Multi-objective optimization results showing the tradeoff between terminal position uncertainty and total propellant usage are presented for multiple sensor suite compositions. Further, guidance parameters selected using the developed robust trajectory design approach may enable acceptable flight performance with fewer and/or lower-quality sensors. The resulting Pareto fronts present the optimal trade space early in the mission design process to enable informed decision-making.

Perseverance MEDA Atmospheric Pressure Observations—Initial Results

AbstractThe Mars2020 Perseverance Rover landed successfully on the Martian surface on the Jezero Crater floor (18.44°N, 77.45°E) at Martian solar longitude, Ls, ∼5° in February 2021. Since then, it has produced highly valuable environmental measurements with a versatile scientific payload including the MEDA (Mars Environmental Dynamics Analyzer) suite of environmental sensors. One of the MEDA systems is the PS pressure sensor system, which weighs 40 g and has an estimated absolute accuracy of better than 3.5 Pa and a resolution of 0.13 Pa. We present initial results from the first 414 sols of Martian atmospheric surface pressure observations by the PS, whose performance was found to meet its specifications. Observed sol‐averaged atmospheric pressures follow an anticipated pattern of pressure variation in the course of the advancing season and are consistent with data from other landing missions. The observed daily pressure amplitude varies by ∼2%–5 % of the sol‐averaged pressure, with absolute amplitude 10–35 Pa in an approximately direct relationship with airborne dust. During a regional dust storm, which began at Ls ∼ 135°, the daily pressure amplitude roughly doubled. The daily pressure variations were found to be remarkably sensitive to the seasonal evolution of the atmosphere. In particular, analysis of the daily pressure signature revealed diagnostic information likely related to the regional scale structure of the atmosphere. Comparison of Perseverance pressure observations with data from other landers reveals the global scale seasonal behavior of Mars' atmosphere.

Safeguarding Systems

Modern vehicles include sensitive electronics that must be protected from temperature, dirt, rain, snow or road salt. Trelleborg Sealing Solutions’s Rubore Washer is suitable wherever electrical sensors, control units or battery systems are enclosed in bolted housings.

Dual axis solar tracking system for agriculture applications using machine learning

The three most basic amenities required for human survival are food, shelter and clothing. In today's tech-savvy generation, these have experienced a great deal of scientific advancement. Unfortunately, agriculture is still more man power-oriented. So they have to rely on the hit and trial method to learn from experience which leads to waste of time. In proposed work includes an automated system using dual axis solar tracking system and gives crop recommendation for different types of soil to yield maximum. The suggested system is a dual-axis solar tracker based on machine learning that is intended to considerably increase the effectiveness of energy harvesting. The approach makes use of the logistic regression algorithm (LR) to do this. This novel strategy tries to maximize the solar panel's ability to produce energy, leading to increased energy yields. The quality of soil is predicted by using suitable sensors for crop recommendation. The data’s are temperature, humidity, pH of soil, nitrogen, phosphorous & potassium in soil and rainfall in soil are considered. For crop recommendation six algorithms- SVM, KNN, Native Bays, Logistic Regression, Decision Tree classifier, Random Forest Classifier are applied and tested. It is found that random forest classifier gave us excellent results.

A framework for multidisciplinary science observations from commercial ships

Abstract Science Research on Commercial Ships (Science RoCS) is a grassroots multi-institution group of scientists, engineers, data managers, and administrators seeking to further research opportunities by equipping commercial vessels with suites of maritime appropriate scientific sensors operated autonomously on regular ship routes with minimal crew intervention. Science RoCS aims to foster cooperation between the shipping industry and scientific community at a level that will be transformative for societally relevant ocean science, promote cross-disciplinary ocean science through simultaneous observation of the air/sea interface and water column, and spur a technological revolution in observational oceanography by developing new turnkey, maritime-industry-appropriate scientific equipment whose data streams can be used to stimulate innovations in oceanic (physical, chemical, and biological) understanding and forecasting. We envision a future where scientific data collection on commercial ships is the new industry standard, providing repeat measurements in undersampled, remote regions, on scales not otherwise accessible to the scientific community.

Ultrasonic and conversion-based inline fouling measurements for continuous emulsion copolymerisation of vinyl acetate in a tubular reactor.

One of the serious challenges for the implementation of continuous emulsion polymerisation is their significant fouling. The elucidation of time-dependent fouling processes and the development of inline analysis methods for fouling mass quantification are crucial to making progress in this area. Inline-sensor concepts based on ultrasonic measurements as well as residence time and conversion analysis were investigated regarding their suitability for the detection of time-dependent fouling formation and compared with gravimetrical results in order to validate their precision. Both a set-up using a turnover analysis for determination of losses in reaction volume by fouling and an ultrasound-based measurement system detecting deposit-caused changes by evaluating the average sound velocity could be used as suitable sensor concepts. The accuracies of both sensors are below 10% deviation to fouling references.

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.

The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2

Abstract. For several decades, a suite of satellite sensors has enabled us to study the global spatiotemporal distribution of phytoplankton through remote sensing of chlorophyll. However, the satellite record has extensive missing data, partially due to cloud cover; regions characterized by the highest phytoplankton abundance are also some of the cloudiest. To quantify potential sampling biases due to missing data, we developed a satellite simulator for ocean chlorophyll in the Community Earth System Model (CESM) that mimics what a satellite would detect if it were present in the model-generated world. Our Chlorophyll Observation Simulator Package (ChlOSP) generates synthetic chlorophyll observations at model runtime. ChlOSP accounts for missing data – due to low light, sea ice, and cloud cover – and it can implement swath sampling. Here, we introduce this new tool and present a preliminary study focusing on long timescales. Results from a 50-year pre-industrial control simulation of CESM–ChlOSP suggest that missing data impact the apparent mean state and variability of chlorophyll. The simulated observations exhibit a nearly −20 % difference in global mean chlorophyll compared with the standard model output, which is the same order of magnitude as the projected change in chlorophyll by the end of the century. Additionally, missing data impact the apparent seasonal cycle of chlorophyll in subpolar regions. We highlight four potential future applications of ChlOSP: (1) refined model tuning; (2) evaluating chlorophyll-based net primary productivity (NPP) algorithms; (3) revised time to emergence of anthropogenic chlorophyll trends; and (4) a test bed for the assessment of gap-filling approaches for missing satellite chlorophyll data.

Colloidal Quantum Dots for Explosive Detection: Trends and Perspectives.

Sensitive, accurate, and reliable detection of explosives has become one of the major needs for international security and environmental protection. Colloidal quantum dots, because of their unique chemical, optical, and electrical properties, as well as easy synthesis route and functionalization, have demonstrated high potential to meet the requirements for the development of suitable sensors, boosting the research in the field of explosive detection. Here, we critically review the most relevant research works, highlighting three different mechanisms for explosive detection based on colloidal quantum dots, namely photoluminescence, electrochemical, and chemoresistive sensing. We provide a comprehensive overview and an extensive discussion and comparison in terms of the most relevant sensor parameters. We highlight advantages, limitations, and challenges of quantum dot-based explosive sensors and outline future research directions for the advancement of knowledge in this surging research field.

A 3D World Interpreter System for Safe Autonomous Crane Operation

In an effort to improve short-sea shipping in Europe, we present a 3D world interpreter (3DWI) system as part of a robotic container-handling system. The 3DWI is an advanced sensor suite combined with AI-based software and the communication infrastructure to connect to both the crane control and the shore control center. On input of LiDAR data and stereo captures, the 3DWI builds a world model of the operating environment and detects containers. The 3DWI and crane control are the core of an autonomously operating crane that monitors the environment and may trigger an emergency stop while alerting the remote operator of the danger. During container handling, the 3DWI scans for human activity and continuously updates a 3D-Twin model for the operator, enabling situational awareness. The presented methodology includes the sensor suite design, creation of the world model and the 3D-Twin, innovations in AI-detection software, and interaction with the crane and operator. Supporting experiments quantify the performance of the 3DWI, its AI detectors, and safety measures; the detectors reach the top of VisDrone’s leaderboard and the pilot tests show the safe autonomous operation of the crane.

A triboelectric smart carpet with an optimized braided structure for cruise ship monitoring enabled by deep learning

In pursuit of comprehensive monitoring of personnel within cruise ship cabins, this study introduces a novel triboelectric smart carpet featuring an intricate braided design. This innovative carpet is allowed to gather individual information without the privacy concerns associated with video recording. The discourse begins with a detailed examination of the output characteristics inherent to the sensing unit, pixel, and the carpet as a whole. The findings suggest that the smart carpet not only precisely identify any walking trajectory but also enhances sensor scalability, showcasing its considerable promise for monitoring over extended floor areas. Further, this study integrates the smart carpet with Deep Learning (DL) techniques to validate its real-time monitoring capabilities, which encompasses state detection, personnel identification, and trajectory tracking. The outcomes demonstrate a commendably high level of precision, indicating a robust performance. Additionally, the smart carpet's design facilitates its integration with a suite of other sensors, positioning it as an instrumental asset for reinforcing maritime safety and fostering the development of Internet of Things (IoT) applications for maritime environments.

A Metal-Free Triazacoronene-Based Bimodal VOC Sensor.

Developing suitable sensors for selective and sensitive detection of volatile organic compounds (VOCs) is crucial for monitoring indoor and outdoor air quality. VOCs are very harmful to our health upon inhalation or contact. Bimodal sensor materials with more than one transduction capability (optical and electrical) offer the ability to extract complementary information from the individual analyte, thus improving detection accuracy and performance. The privilege of manipulating the optoelectronic properties of the polycyclic aromatic hydrocarbon-based semiconducting materials offers rapid signal transduction in multimodal sensing applications. A thiophene-functionalized triazacoronene (TTAC) donor-acceptor-donor (D-A-D) type sensor is reported here for VOC sensing. The single-crystal X-ray structure analysis of the TTAC revealed that a distinctive supramolecular polymer architecture was formed because of cooperative π-π and intermolecular D-A interactions and exhibited rapid signal transduction upon exposure to specific VOCs. The TTAC-embedded green luminescent paper-based test strip exhibited an on-off fluorescence response upon nitrobenzene vapor exposure for 120 s. The selective and rapid response is due to the fast photoinduced electron transfer, as is evident from the time-resolved excited-state dynamics and density functional theory studies. The thick-film-based prototype chemiresistive sensor detects harmful VOCs in a custom-made gas sensing system including benzene, toluene, and nitrobenzene. The TTAC sensor rapidly responds (200 s) at relatively low temperatures (180 οC) compared to other reported metal-oxide-based sensors.

Unravelling performance of honeycomb structures as drug delivery systems for the isoniazid drug using DFT-D3 correction dispersion and molecular dynamic simulations.

In this study, the potential of aluminum nitride (h-AlN), boron nitride (h-BN) and silicon carbide (h-SiC) nanosheets as the drug delivery systems (DDS) of isoniazid (INH) was scrutinized through density functional theory (DFT) and molecular dynamic (MD) simulations. We performed DFT periodic calculations on the geometry and electronic features of nanosheets adsorbed with INH by the DFT functional (DZP/GGA-PBE) employed in the SIESTA code. In the energetically favorable model, an oxygen atom of the C-O group of the INH molecule interacts with a Si atom of the h-SiC at 2.077 Å with an interaction energy of -1.361 eV. Charge transfer (CT) calculation by employing the Mulliken, Hirshfeld and Voronoi approaches reveals that the monolayers and drug molecules act as donors and acceptors, respectively. The density of states (DOS) calculations indicate that the HOMO-LUMO energy gap (HLG) of the h-SiC nanosheet declines significantly from 2.543 to 1.492 eV upon the adsorption of the INH molecule, which causes an electrical conductivity increase and then produces an electrical signal. The signal is linked to the existence of INH, demonstrating that h-SiC may be an appropriate sensor for INH sensing. The decrease in HLG for the interaction of INH and h-SiC is the uppermost (up to 41%) representing the uppermost sensitivity, whereas the sensitivity trend is σ(h-SiC) > σ(h-AlN) > σ(h-BN). Quantum theory of atoms in molecules (QTAIM) investigations is employed to scrutinize the nature of the INH/nanosheet interactions. The QTAIM analysis reveals that the interaction of the INH molecule and h-SiC has a partially covalent nature, while INH/h-AlN model electrostatic interaction occurs in the system and noncovalent and electrostatic interaction for the INH/h-BN model. Finally, the state-of-the-art DFT-MD simulations utilized in this study can mimic ambient conditions. The results obtained from the MD simulation show that it takes more time to bond the INH drug and h-SiC, and the INH/h-SiC system becomes stable. The results of the current research demonstrate the potential of h-SiC as a suitable sensor and drug delivery platform for INH drugs to remedy tuberculosis.

Experimental Nonrobocentric Dynamic Landing of Quadrotor UAVs With On-Ground Sensor Suite

Experimental Nonrobocentric Dynamic Landing of Quadrotor UAVs With On-Ground Sensor Suite

Abnormality detection of sliding surface and exploration suitable sensor data for condition monitoring by calculating contribution using machine learning

Abnormality detection of sliding surface and exploration suitable sensor data for condition monitoring by calculating contribution using machine learning

Designing the Operator of the Future: The Architecture of Human Digital Twin Systems

The central role that the human operator assumed in industrial design and development processes, driven by Industry 5.0 principles, has paved the way for the creation of new human-centric system architectures. This paper discusses the practical implications regarding the deployment of a Human Digital Twin-based system in the manufacturing environment. It provides a proof-of-concept of the architecture for the implementation of the twinning process, starting from the investigation of the most suitable hardware selection based on the desired outcomes to build up the human monitoring phase. We analyze three management decision-making levels to determine the scalability of the proposed architecture for strategical, technical, and operational managerial strategies. This research aims to propose some technological selection criteria, based on the main characteristics of the available technological acquisition devices, to determine the most suitable sensors for the creation of the physical twinning monitoring process.

Detection of SO2F2 Using a Photoacoustic Two-Chamber Approach.

The wide use of sulfuryl difluoride (SO2F2) for termite control in buildings, warehouses and shipping containers requires the implementation of suitable sensors for reliable detection. SO2F2 is highly toxic to humans and the environment, and moreover, it is a potent greenhouse gas. We developed two photoacoustic two-chamber sensors with the aim to detect two different concentration ranges, 0-1 vol.-% SO2F2 and 0-100 ppm SO2F2, so that different applications can be targeted: the sensor for high concentrations for the effective treatment of buildings, containers, etc., and the sensor for low concentrations as personal safety device. Photoacoustic detectors were designed, fabricated, and then filled with either pure SO2F2 or pure substituent gas, the refrigerant R227ea, to detect SO2F2. Absorption cells with optical path lengths of 50 mm and 1.6 m were built for both concentration ranges. The sensitivity to SO2F2 as well as cross-sensitivities to CO2 and H2O were measured. The results show that concentrations below 1 ppm SO2F2 can be reliably detected, and possible cross-sensitivities can be effectively compensated.

Theoretical and Experimental Studies of Molecular Interactions between Engineered Graphene and Phosphate Ions for Graphene-Based Phosphate Sensing.

Fundamental understanding of the interactions of nanoscale materials with molecules of interest is essential for the development of electronic devices, such as sensors. In particular, structures and molecular interaction properties of engineered graphenes are still largely unexplored, despite these materials' great potential to be used as molecular sensors. As an example of end user application, the detection of phosphorus in the form of phosphate in a soil environment is important for soil fertility and plant growth. However, due to the lack of an affordable technology, it is currently hard to measure the amount of phosphate directly in the soil; therefore, suitable sensor technologies need to be developed for phosphate sensors. In this work, pristine graphene and several modified graphene materials (oxygenated graphene, graphene with vacancies, and curved graphene) were studied as candidates for phosphate sensor materials using density functional theory (DFT) calculations. Our calculations showed that both pristine graphene and functionalized graphene were able to adsorb phosphate species strongly. In addition, these graphene nanomaterials showed selectivity of adsorption of phosphate with respect to nitrate, with stronger adsorption energies for phosphate. Furthermore, our calculations showed significant changes in electrical conductivities of pristine graphene and functionalized graphenes after phosphate species adsorption, in particular, on graphene with oxygen (hydroxyl and epoxide) functional groups. Experimental measurements of electrical resistivity of graphene before and after adsorption of dihydrogen phosphate showed an increase in resistivity upon adsorption of phosphate, consistent with the theoretical predictions. Our results recommend graphene and functionalized graphene-based nanomaterials as good candidates for the development of phosphate sensors.