Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods. Full article

Nutrient pollution remains one of the greatest threats to water quality and imposes numerous public health and ecological concerns. Phosphate, the most common form of phosphorus, is one of the key nutrients necessary for plant growth. However, phosphate concentration in water should be carefully monitored for environmental protection requirements. Hence, an easy-to-use, field-deployable, and reliable device is needed to measure phosphate concentrations in the field. In this study, an inexpensive dip strip is developed for the detection of low concentrations of phosphate in water and seawater. In this device, ascorbic acid/antimony reagent was dried on blotting paper, which served as the detection zone, and was followed by a wet chemistry protocol using the molybdenum method. Ammonium molybdate and sulfuric acid were separately stored in liquid form to significantly improve the lifetime of the device and enhance the reproducibility of its performance. The device was tested with deionized water and Sargasso Sea seawater. The limits of detection and quantification for the optimized device using a desktop scanner were 0.134 ppm and 0.472 ppm for phosphate in water and 0.438 ppm and 1.961 ppm in seawater, respectively. The use of the portable infrared lightbox previously developed at our lab improved the limits of detection and quantification by a factor of three and were 0.156 ppm and 0.769 ppm for the Sargasso Sea seawater. The device’s shelf life, storage conditions, and limit of detection are superior to what was previously reported for the paper-based phosphate detection devices. Full article

Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection. Full article

Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model. Full article

A novel promising carbon paste electrode with excellent potentiometric properties was prepared for the analysis of trihexyphenidyl hydrochloride (THP), the acetylcholine receptor and an anticholinergic drug in real samples. It contains 10.2% trihexyphenidy-tetraphenylborate ionic pair as the electroactive material, with the addition of 3.9% reduced graphene oxide and 0.3% of anionic additive into the paste, which consists of 45.0% dibutylphthalate as the solvent mediator and 40.6% graphite. Under the optimized experimental conditions, the electrode showed a Nernstian slope of 58.9 ± 0.2 mV/decade with a regression coefficient of 0.9992. It exhibited high selectivity and reproducibility as well as a fast and linear dynamic response range from 4.0 × 10⁻⁷ to 1.0 × 10⁻² M. The electrode remained usable for up to 19 days. Analytical applications showed excellent recoveries ranging from 96.8 to 101.7%, LOD was 2.5 × 10⁻⁷ M. The electrode was successfully used for THP analysis of pharmaceutical and biological samples. Full article

Sensor networks provide services to a broad range of applications ranging from intelligence service surveillance to weather forecasting. While most of the sensor networks are terrestrial, Underwater Sensor Networks (USN) are an emerging area. One of the unavoidable and increasing challenges for modern USN technology is tolerating faults, i.e., accepting that hardware is imperfect, and coping with it. Fault Tolerance tends to have more impact in underwater than in terrestrial environment as the latter is generally more forgiving. Moreover, reaching the malfunctioning devices for replacement and maintenance under water is harder and more costly. The current paper is the first to provide an overview of fault-tolerant, particularly cross-layer fault-tolerant, techniques in USNs. In the paper, we present a systematic survey of the techniques, introduce a taxonomy of the Fault Tolerance tasks, present a categorized list of articles, and list the open research issues within the area. Full article

The ball-catching system examined in this research, which was composed of an omni-directional wheeled mobile robot and an image processing system that included a dynamic stereo vision camera and a static camera, was used to capture a thrown ball. The thrown ball was tracked by the dynamic stereo vision camera, and the omni-directional wheeled mobile robot was navigated through the static camera. A Kalman filter with deep learning was used to decrease the visual measurement noises and to estimate the ball’s position and velocity. The ball’s future trajectory and landing point was predicted by estimating its position and velocity. Feedback linearization was used to linearize the omni-directional wheeled mobile robot model and was then combined with a proportional-integral-derivative (PID) controller. The visual tracking algorithm was initially simulated numerically, and then the performance of the designed system was verified experimentally. We verified that the designed system was able to precisely catch a thrown ball. Full article

Mapping the environment is necessary for navigation, planning and manipulation. In this paper, a fusion framework (as data-in-decision-out) is introduced for a 2D LIDAR and a 3D ultrasonic sensor to achieve three-dimensional mapping without expensive 3D LiDAR scanner or visual processing. Two sensor models are proposed for the two sensors used for map updating. Furthermore, 2D/3D map representations are discussed for our fusion approach. We also compare different probabilistic fusion methods and discuss criterias for choosing appropriate methods. Experiments are carried out with a real ground robot platform in an indoor environment. The 2D and 3D map results demonstrate that our approach is able to show the surrounding in more details. Sensor fusion provides a better estimation of the environment and the ego-pose whilst lowering the necessary resources. This gives the robot’s perception of the environment more information by using only one additional low-cost 3D ultrasonic sensor. This is especially important for robust and light-weight robots with limited resources. Full article

Demographic changes are putting the healthcare industry under pressure. However, while other industries have been able to automate their operation through robotic and autonomous systems, the healthcare sector is still reluctant to change. What makes robotic innovation in healthcare so difficult? Despite offering more efficient, and consumer-friendly care, the assistive robotics market has lacked penetration. To answer this question, we have broken down the development process, taking a market transformation perspective. By interviewing assistive robotics companies at different business stages from France and the UK, this paper identifies new insight into the main barriers of the assistive robotics market that are inhibiting the sector. Their impact is analysed during the different stages of the development, exploring how these barriers affect the planning, conceptualisation and adoption of these solutions. This research presents a foundation for understanding innovation barriers that high-tech ventures face in the healthcare industry, and the need for public policy measures to support these technology-based firms. Full article

Adhesively bonded dissimilar materials have attracted high interest in the aerospace and automotive industries due to their ability to provide superior structural characteristics and reduce the weight for energy savings. This work focuses on the improvement of disbond-type defect detectability using the immersion pulse-echo ultrasonic technique and an advanced post-processing algorithm. Despite the extensive work done for investigation, it is still challenging to locate such defects in dissimilar material joints due to the large differences in the properties of metals and composites as well as the multi-layered structure of the component. The objective of this work is to improve the detectability of defects in adhesively bonded aluminum and carbon fiber-reinforced plastic (CFRP) by the development of an advanced post-processing algorithm. It was determined that an analysis of multiple reflections has a high potential to improve detectability according to results received by inspection simulations and the evaluation of boundary characteristics. The impact of a highly influential parameter such as the sample curvature can be eliminated by the alignment of arrival time of signals reflected from the sample. The processing algorithm for the improvement of disbond detectability was developed based on time alignment followed by selection of the time intervals with a significant amplitude change of the signals reflected from defective and defect-free areas and shows significant improvement of disbond detectability. Full article

This paper present simulation-based results on the impact of transmitter (Tx) position and orientation uncertainty on the accuracy of the visible light positioning (VLP) system based on the received signal strength (RSS). There are several constraining factors for RSS-based algorithms, particularly due to multipath channel characteristics and set-up uncertainties. The impact of Tx uncertainties on positioning error performance is studied, assuming a statistical modelling of the uncertainties. Simulation results show that the Tx uncertainties have a severe impact on the positioning error, which can be leveraged through the usage of more transmitters. Concerning a smaller Tx’s position uncertainty of 5 cm, the average positioning errors are 23.3, 15.1, and 13.2 cm with the standard deviation values of 6.4, 4.1, and 2.7 cm for 4-, 9-, and 16-Tx cases, respectively. While for a smaller Tx’ orientation uncertainty of 5°, the average positioning errors are 31.9, 20.6, and 17 cm with standard deviation values of 9.2, 6.3, and 3.9 cm for 4-, 9-, and 16-Tx cases, respectively. Full article

Diabetes patients are at risk of having chronic wounds, which would take months to years to resolve naturally. Chronic wounds can be countered using the electrical stimulation technique (EST) by dielectrophoresis (DEP), which is label-free, highly sensitive, and selective for particle trajectory. In this study, we focus on the validation of polystyrene particles of 3.2 and 4.8 μm to predict the behavior of keratinocytes to estimate their crossover frequency (f_XO) using the DEP force (F_DEP) for particle manipulation. MyDEP is a piece of java-based stand-alone software used to consider the dielectric particle response to AC electric fields and analyzes the electrical properties of biological cells. The prototypic 3.2 and 4.8 μm polystyrene particles have f_XO values from MyDEP of 425.02 and 275.37 kHz, respectively. Fibroblast cells were also subjected to numerical analysis because the interaction of keratinocytes and fibroblast cells is essential for wound healing. Consequently, the predicted f_XO from the MyDEP plot for keratinocyte and fibroblast cells are 510.53 and 28.10 MHz, respectively. The finite element method (FEM) is utilized to compute the electric field intensity and particle trajectory based on DEP and drag forces. Moreover, the particle trajectories are quantified in a high and low conductive medium. To justify the simulation, further DEP experiments are carried out by applying a non-uniform electric field to a mixture of different sizes of polystyrene particles and keratinocyte cells, and these results are well agreed. The alive keratinocyte cells exhibit N_DEP force in a highly conductive medium from 100 kHz to 25 MHz. 2D/3D motion analysis software (DIPP-MotionV) can also perform image analysis of keratinocyte cells and evaluate the average speed, acceleration, and trajectory position. The resultant N_DEP force can align the keratinocyte cells in the wound site upon suitable applied frequency. Thus, MyDEP estimates the Clausius–Mossotti factors (CMF), FEM computes the cell trajectory, and the experimental results of prototypic polystyrene particles are well correlated and provide an optimistic response towards keratinocyte cells for rapid wound healing applications. Full article

This paper considers the finite-time dynamic tracking control for parallel robots with uncertainties and input saturation via a finite-time nonsingular terminal sliding mode control scheme. A disturbance observer is designed to estimate the lumped disturbance in the dynamic model of the parallel robot, including modeling errors, friction and external disturbance. By introducing the fractional exponential powers into the existing asymptotic convergent auxiliary system, a novel finite-time convergent auxiliary system is constructed to compensate for input saturation. The finite-time nonsingular terminal sliding mode control is proposed based on the disturbance estimation and the state of the novel auxiliary system, so that the convergence performance, control accuracy and robustness are improved. Due to the estimation and compensation for the lumped disturbance, the inherent chattering characteristic of sliding mode control can be alleviated by reducing the control gain. The finite-time stability of the closed-loop system is proved with Lyapunov theory. Finally, simulation and experimental research on the dynamic control of a conveying parallel robot are carried out to verify the effectiveness of the proposed method. Full article

The reliable monitoring of sea state parameters is a key factor for weather forecasting, as well as for ensuring the safety and navigation of ships. In the current analysis, two spectrum estimation techniques, based on the Welch and Thomson methods, were applied to a set of random wave signals generated from a theoretical wave spectrum obtained by combining wind sea and swell components with the same prevailing direction but different combinations of significant wave heights, peak periods, and peak enhancement factors. A wide benchmark study was performed to systematically apply and compare the two spectrum estimation methods. In this respect, different combinations of wind sea spectra, corresponding to four grades of the Douglas Scale, were combined with three swell spectra corresponding to different swell categories. The main aim of the benchmark study was to systematically investigate the effectiveness of the Welch and Thomson methods in terms of spectrum restitution and the assessment of sea state parameters. The spectrum estimation methods were applied to random wave signals with different durations, namely 600 s (short) and 3600 s (long), to investigate how the record length affected the assembled sea state parameters, which, in turn, were assessed by the nonlinear least square method. Finally, based on the main outcomes of the benchmark study, some suggestions are provided to select the most suitable spectrum reconstruction method and increase the effectiveness of the assembled sea state parameters. Full article

Hyperspectral LiDAR (HSL) is a new remote sensing detection method with high spatial and spectral information detection ability. In the process of laser scanning, the laser echo intensity is affected by many factors. Therefore, it is necessary to calibrate the backscatter intensity data of HSL. Laser incidence angle is one of the important factors that affect the backscatter intensity of the target. This paper studied the radiometric calibration method of incidence angle effect for HSL. The reflectance of natural surfaces can be simulated as a combination of specular reflection and diffuse reflection. The linear combination of the Lambertian model and Beckmann model provides a comprehensive theory that can be applied to various surface conditions, from glossy to rough surfaces. Therefore, an adaptive threshold radiometric calibration method (Lambertian–Beckmann model) is proposed to solve the problem caused by the incident angle effect. The relationship between backscatter intensity and incident angle of HSL is studied by combining theory with experiments, and the model successfully quantifies the difference between diffuse and specular reflectance coefficients. Compared with the Lambertian model, the proposed model has higher calibration accuracy, and the average improvement rate to the samples in this study was 22.67%. Compared with the results before calibration with the incidence angle of less than 70°, the average improvement rate of the Lambertian–Beckmann model was 62.26%. Moreover, we also found that the green leaves have an obvious specular reflection effect near 650–720 nm, which might be related to the inner microstructure of chlorophyll. The Lambertian–Beckmann model was more helpful to the calibration of leaves in the visible wavelength range. This is a meaningful and a breakthrough exploration for HSL. Full article