Search Results (74)

Road accidents could result in severe or fatal neck injuries. A few surrogate necks are available to develop and test neck protectors as countermeasures, but each has its own limitations. The objective of this study was to develop a surrogate neck compatible with the Hybrid III dummy, focused on tunable flexural stiffness and integrated angular sensors for kinematic feedback during impact tests. The neck features six 3D-printed surrogate vertebral bodies interconnected by rubber surrogate discs, providing a baseline flexibility to the surrogate fundamental spinal units. An adjustable inner cable and elastic elements hooked on the sides of vertebral elements allow to increase the flexural stiffness of the surrogate and to simulate the asymmetric behavior of the human neck. Neck flexural angles and axial compression are measured using a novel system made of wires, pulleys, and rotary potentiometers embedded in the neck base. A motion capture system and a load cell were used to determine the bending and torsional stiffness of the neck and to calibrate the sensors. Results showed that the neck flexural stiffness can be tuned between 3.29 and 5.76 Nm/rad. Torsional stiffness was 1.01 Nm/rad and compression stiffness can be tuned from 39 to 193 N/mm. Sensor flexural angles were compared with motion capture angles, showing an RMSE error of 1.35° during static testing and of 3° during dynamic testing. The developed neck could be a viable tool for investigating neck braces from a kinematic and kinetic perspective due to its inbuilt sensing ability and its tunable stiffness. Full article

This paper presents a novel multi-sensor system for enhanced maneuver analysis in Olympic dinghy sailing. In the ILCA class, there is an increasing demand for precise in-field measurement and analysis of physical properties beyond well-established velocity and course metrics. The low-cost setup presented in this study consists of a combination of commercially available sensor systems, such as the AdMos sensor for IMU and GNSS measurement, in combination with custom measurement systems for rudder and mast rotations using fully waterproofed potentiometers. Data streams are synchronized using GNSS time stamping for streamlined analysis. The resulting analysis presents a selection of 12 upwind tacks, with corresponding path overlays, detailed timeseries data, and performance metrics. The system has demonstrated the value of extended data analysis of in situ data with an elite ILCA 7 sailor. The addition of rudder and mast rotations has enabled enhanced analysis of on-water maneuvers for single-handed Olympic dinghies like the ILCA 7, on a level of detail previously reserved for simulated environments. Full article

Resistive angle sensors are widely used due to their simple signal conditioning circuits and high cost-effectiveness. This paper presents a resistive angle sensor based on a rotary potentiometer, designed to offer a measurement range of 180° for low-cost angle measurement in industrial automation and electromagnetic interference (EMI)-sensitive applications. The sensor features a specially designed signal conditioning circuit and mechanical housing. Experimental results show that it exhibits excellent linearity and temperature stability over a wide temperature range of −20 °C to 60 °C, with a zero-temperature drift of approximately 0.004°/°C. For the nonlinearity and hysteresis caused by unavoidable friction and manufacturing tolerances between the transmission mechanism and rotary potentiometer, an adaptive linear neuron (ADALINE) technique based on the α-least mean square (α-LMS) algorithm was implemented for software compensation. The results show that the percentage nonlinearity error was reduced from the original 4.413% to 0.182%, and the percentage hysteresis error was decreased from the original 4.061% to 0.404%. The research results of this paper offer valuable insight for high-precision resistive angle sensors. Full article

Devices with tunable characteristics and parameters are used in many technical fields. Such devices can be based on memristors, which serve as programmable potentiometers. The quality of the tuning is higher by means of memristors than with mechanical and digital potentiometers. We investigate a bandpass filter in the form of an active Wien bridge with a memristor. The filter is analyzed with the help of the nodal voltage method. The dependence of the resonance frequency on the parameters of the Wien circuit, the dependence of the quality factor, and the filter gain at resonant frequency on the parameters of the voltage divider are obtained. The dependences of the resonant frequency, quality factor, and gain at the resonant frequency on the parameters of the Wien filter were formed. The tuning of the main frequency features (the filter gain, quality factor, and resonance frequency) is shown to be independent. Under different values of memristance, the frequency features result from a simulation in LTspice. These features are less than 1 percent different from the corresponding features obtained analytically. Thus, the high precision of modeling and tuning of the frequency characteristics of the memristive Wien filter is demonstrated. Full article

Modern agricultural machines are subject to requirements that result from developments in plant cultivation technology and environmental care. Agricultural practice demands multifunctional machines that perform several agrotechnical treatments in a single pass. Automated and digitalised management of machines and their working parts is also becoming standard. A strip-till one-pass machine was designed that automatically regulates and monitors sowing rate and depths and the application of fertiliser to loosened soil strips. Among other things, an electro-hydraulic depth regulator with a built-in linear potentiometer and an overload sensor was used. Laboratory and field tests assessed the accuracy of the rate and depth of sowing wheat grain and fertiliser application by the innovative machine. This study confirmed the machine’s high quality of wheat sowing. The accuracy of the operating parameters was not less than 97% in laboratory tests and 92% in field conditions. The field emergence capacity of wheat was 88% and its sowing density can be considered good. The machine provides uniform operation of all 11 multifunctional assemblies (units, sections of loosening-applying tines and sowing coulters). The coefficient of variation (CV) of grain sowing and granular fertiliser application by individual assemblies was in the range of 4.27–7.29% and 3.74–6.90%, respectively. The sowing depth accuracy expressed as an accuracy coefficient (DA) was 87.33–93.67% with CV 4.62–9.65%. The machine’s introduction onto the market can facilitate field cultivation of plants in accordance with the principles of conservation agriculture and Agriculture 4.0. Full article

Human hands have the unique ability to classify material properties, such as hardness, using mechanoreceptors and tactile information. Previous studies have demonstrated hardness classification using Commercial Off-The-Shelf (COTS) sensors but lacked robotic integration considerations. This study explores the integration of multiple COTS sensors, inspired by mechanoreceptors, for classifying material hardness. The sensors were used to classify objects into three categories—hard, soft, and flexible—based on the qualitative Shore hardness scale. The aim was to identify the optimal sensor topology configuration that delivers high accuracy, using machine learning algorithms provided in the literature. The results suggest that the Random Forest Classifier is the most suitable algorithm, showcasing accuracies ranging from 90% to 98.7%, across various sensor topologies. The ‘PFV’ topology, comprising a potentiometer (P), force sensor (F), and vibration sensor (V), achieved the highest accuracy of 98.7%, while the ‘FPV’ and ‘FVP’ recorded accuracies between 96% and 97.5%. The topology of FPV and FVP have the most closely related configuration to that of mechanoreceptors; however, the results show that PFV outperforms this configuration. While the PFV topology marginally outperforms the mechanoreceptor-inspired configurations, the results demonstrate that bio-inspired sensor arrangements provide a robust solution for hardness classification in robotics. The PFV topology performs better than FPV in terms of prediction speed, with an average prediction time of 8.31 ms (millisecond) for PFV versus 13.93 ms for FPV. PFV and FPV achieved 12 and 13 correct predictions, respectively, out of 18 objects. The faster prediction times of PFV make it particularly advantageous for applications requiring quick and accurate decision-making for robotic applications. Full article

The management and classification of solid waste is one of the most important challenges worldwide. The objective is to design a basic waste classification system at the source using a low-cost experimental capacitive sensor and machine learning algorithms. For this, two types of sensor models were established (Traditional Model (MT) and Non-Traditional Model (MNT)), which were built with recyclable material and tested with different types of materials, in order to evaluate their behavior and sensitivity level. The results obtained demonstrated that the two sensors responded with acceptable sensitivity levels for each of the materials used as a test; however, the MNT was the one that generated the values with the greatest variability, an aspect that is deemed highly significant, because, thanks to this type of response to various types of materials, it facilitates the classification processes through the use of machine learning algorithms. Finally, the two prototypes of sensors manufactured can be considered of significant relevance for the development of more complex solutions, related to the classification and possible characterization of materials, when compared to the capacitive sensors found on the market, which only then allow us to identify if there is a presence or not of some object through adjustment by potentiometer, generating as a result a digital output. This aspect largely limits the use of commercial capacitive sensors to applications exclusively related to presence or level detection. Full article

This research focuses on the design and implementation of a movement strategy for a photovoltaic (PV) system, presented through four phases: First came the design of the mechanical part and the selection of geared motors with high torque and low power consumption, while having a solid mechanical structure that supports the panel. An open-loop control was selected using solar positioning equations, with the inputs defined as solar equations. The Intel Edison development board was chosen for programming the solar equations in Python. Two linear potentiometers served as sensing elements, where analog–digital characterization was conducted for each movement. The plant started with the static panel at a latitude of 7° south, oriented toward the equator, achieving a performance of 177.62 kWh. With the solar tracker, a performance of 232.38 kWh was obtained, resulting in an efficiency increase of 27%. Given the aim of enhancing PV efficiency through increased utilization, satisfactory results were achieved. Another advantage of the unit is that it is designed to support more than one panel. Full article

This paper presents PCB design solutions for implementing a radiative-field RF energy harvester with an ASK-PWM decoding communication scheme using available commercial components. The paper provides the design approach and tackles key challenges such as the impact of inductive parasitic effects at the output of the harvester, how to maintain the $PCE$ at a constant value regardless of the time constant at the output of the communication path’s rectifier, and the difficulty of changing the aspect ratio of the discrete inverter used for PWM decoding. These challenges are addressed by using multiple capacitors connected in parallel at the output of the rectifier to reduce inductive parasitic effects, adding a series resistor in the communication path’s rectifier to isolate its loading from impacting the $PCE$, and utilizing a potentiometer in the inverter to realize PWM decoding on PCB. The system was manufactured using FR-4 substrate material with a size of 5 cm × 4 cm × 0.6 cm, harvesting energy at the ISM frequency of 924 MHz with a $PCE$ of $42.12\%$ at a bit rate of 15 Kbps. Moreover, the system consumes only 355 μW of power and maintains correct harvesting and decoding operation in the antenna separation range of 6–12 cm. This work aims to provide an alternative to IC realization by implementing the system entirely using commercial discrete components, reducing costs, adding flexibility, reducing development time, and allowing for simple debugging. Full article

In recent years, plant essential oils have been confirmed as natural inhibitors of foodborne pathogens. Citrus reticulata Blanco cv. Tankan peel essential oil (CPEO) showed anti-Listeria monocytogenes (LM) activities, and this study investigated the associated mechanisms by using high-resolution electron microscope, fluorescence spectrometer, flow cytometer, potentiometer, and transcriptome sequencing. The results showed that CPEO restrained LM growth at a minimum inhibitory concentration of 2% (v/v). The anti-LM abilities of CPEO were achieved by disrupting the permeability of the cell wall, damaging the permeability, fluidity, and integrity of the cell membrane, disturbing the membrane hydrophobic core, and destroying the membrane protein conformation. Moreover, CPEO could significantly inhibit the LM aggregation from forming biofilm by reducing the extracellular polymeric substances’ (protein, polysaccharide, and eDNA) production and bacterial surface charge numbers. The RNA sequencing data indicated that LM genes involved in cell wall and membrane biosynthesis, DNA replication and repair, quorum sensing and two-component systems were expressed differently after CPEO treatment. These results suggested that CPEO could be used as a novel anti-LM agent and green preservative in the food sector. Further studies are needed to verify the anti-LM activities of CPEO in real food. Full article

There are many examples in nature in which the ability to detect is combined with decision-making, such as the basic survival instinct of plants and animals to search for food. We can technically translate this innate function via the use of robotics with integrated sensors and artificial intelligence. However, the integration of sensing capabilities into robotics has traditionally been neglected due to the significant associated technical challenges. Inspired by plant-root chemotropism, we present a miniaturized electrochemical array integrated into a robotic tip, embedding a customized micro-potentiometer. The system contains solid-state sensors fitted to the tip of the robotic root to three-dimensionally monitor potassium and pH changes in a moist, soil-like environment, providing an integrated electronic readout. The sensors measure a range of parameters compatible with realistic soil conditions. The sensors’ response can trigger the movement of the robotic root with a control algorithm inspired by the behavior of the plant root that determines the optimal path toward root growth, simulating the decision-making process of a plant. This nature-inspired technology may lead, in the future, to the realization of robotic devices with the potential for monitoring and exploring the soil autonomously. Full article

To investigate the functionality and performance of a prototype microjet engine, we constructed a versatile test stand tailored to the specifications of a 400 N prototype. This test stand facilitated a comprehensive study by enabling real-time recording of 45 essential parameters for analysis, encompassing temperatures, pressures, speed, fuel flow, thrust, vibration, and various other monitored metrics. All parameters and control elements were seamlessly integrated via a data acquisition and control system, utilizing a compactDAQ (Data Acquisition) system from National Instruments and a custom Virtual Instrument programmed with graphical language. The test stand offers both manual and automated operation modes, with the flexibility for hybrid operation. For instance, following the idle regime, manual control using a potentiometer can seamlessly transition from automated control via a proportional control (P control) mechanism. Before the experimental campaign, rigorous verification and validation tests were conducted to ensure the reliability and accuracy of the setup. The experimental campaign comprised a series of manual tests focusing on the fuel system and automated tests covering starting, idle, working, and stopping regimes. This structured approach allowed for a comprehensive evaluation across different operational scenarios, providing insights into the engine’s behavior and performance under varying conditions. Full article

A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human–machine system. However, the question remains of what can be accomplished with just one sensor. The objective of this study was to deploy a modular testing approach to test the performance of two joint angle estimation models—a kinematic extrapolation algorithm and a Random Forest machine learning algorithm—when each was informed solely with kinematic gait data from a single potentiometer on an ankle exoskeleton mock-up. This study demonstrates (i) the feasibility of implementing a modular approach to exoskeleton mock-up evaluation to promote continuity between testing configurations and (ii) that a Random Forest algorithm yielded lower realized errors of estimated joint angles and a decreased actuation time than the kinematic model when deployed on the physical device. Full article

Bio-inspired strategies for robotic sensing are essential for in situ manufactured sensors on the Moon. Sensors are one crucial component of robots that should be manufactured from lunar resources to industrialize the Moon at low cost. We are concerned with two classes of sensor: (a) position sensors and derivatives thereof are the most elementary of measurements; and (b) light sensing arrays provide for distance measurement within the visible waveband. Terrestrial approaches to sensor design cannot be accommodated within the severe limitations imposed by the material resources and expected manufacturing competences on the Moon. Displacement and strain sensors may be constructed as potentiometers with aluminium extracted from anorthite. Anorthite is also a source of silica from which quartz may be manufactured. Thus, piezoelectric sensors may be constructed. Silicone plastic (siloxane) is an elastomer that may be derived from lunar volatiles. This offers the prospect for tactile sensing arrays. All components of photomultiplier tubes may be constructed from lunar resources. However, the spatial resolution of photomultiplier tubes is limited so only modest array sizes can be constructed. This requires us to exploit biomimetic strategies: (i) optical flow provides the visual navigation competences of insects implemented through modest circuitry, and (ii) foveated vision trades the visual resolution deficiencies with higher resolution of pan-tilt motors enabled by micro-stepping. Thus, basic sensors may be manufactured from lunar resources. They are elementary components of robotic machines that are crucial for constructing a sustainable lunar infrastructure. Constraints imposed by the Moon may be compensated for using biomimetic strategies which are adaptable to non-Earth environments. Full article

The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the vision-based SHM of bridges along with their systematic procedures. The proposed procedures are implemented via a two-span accelerated bridge construction (ABC) system undergoing a large-scale shake-table test. The research objectives are to explore the effect of refined matching algorithms on blob-based features in improving their accuracies and to implement the proposed algorithms on large-scale bridges tested under seismic loads using vision-based SHM. The procedure begins by adopting blob-based feature detectors, i.e., the scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE algorithms, and their stability is compared. The least medium square (LMEDS), least trimmed square (LTS), random sample consensus (RANSAC), and its generalization maximum sample consensus (MSAC) algorithms are applied for model fitting, and their sensitivity for removing outliers is analyzed. The raw data are corrected using mathematical models and scaled to generate displacement data. Finally, seismic vibrations of the bridge are generated, and the seismic responses are compared. The data are validated using target-tracking methods and mechanical sensors, i.e., string potentiometers. The results show a good agreement between the proposed blob feature detection and matching algorithms and target-tracking data and reference data obtained using mechanical sensors. Full article