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We present a falling-sphere viscometer with a magnetized sphere and fluxgate magnetometers continuously measuring the magnetic field produced at the sensor positions by the falling sphere. With a fluid volume of 15 ml and within a few seconds, we directly measure dynamic viscosities in a range between 200 and 3000 cP with a precision of 3%. [ABSTRACT FROM AUTHOR]

Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three‐dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component‐wise learning combining the initial weight transfer, to overcome the difficulty particularly found in the bridge structures, where a whole structure consists of multiple and various components. The learning method was developed and verified using point cloud data acquired in an actual concrete bridge based on the point cloud completion performance of three significant deep learning models. The effectiveness and applicability of the proposed method were shown in that it improved performances in component level in applying it to the bridge point cloud completion by the multiple deep learning models, respectively. [ABSTRACT FROM AUTHOR]

We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by partial differential equations (PDEs) under model uncertainty. Specifically, we consider inverse problems in which, in addition to the inversion parameters, the governing PDEs include secondary uncertain parameters. We focus on problems with infinite-dimensional inversion and secondary parameters and present a scalable computational framework for optimal design of such problems. The proposed approach enables Bayesian inversion and OED under uncertainty within a unified framework. We build on the Bayesian approximation error (BAE) approach, to incorporate modeling uncertainties in the Bayesian inverse problem, and methods for A-optimal design of infinite-dimensional Bayesian nonlinear inverse problems. Specifically, a Gaussian approximation to the posterior at the maximum a posteriori probability point is used to define an uncertainty aware OED objective that is tractable to evaluate and optimize. In particular, the OED objective can be computed at a cost, in the number of PDE solves, that does not grow with the dimension of the discretized inversion and secondary parameters. The OED problem is formulated as a binary bilevel PDE constrained optimization problem and a greedy algorithm, which provides a pragmatic approach, is used to find optimal designs. We demonstrate the effectiveness of the proposed approach for a model inverse problem governed by an elliptic PDE on a three-dimensional domain. Our computational results also highlight the pitfalls of ignoring modeling uncertainties in the OED and/or inference stages. [ABSTRACT FROM AUTHOR]

In sheet metal forming, the interaction between virtual models and the real world remains challenging. Process simulations can exhibit significant errors, and reliable measurements are often scarce during early production stages. This study presents a hybrid twin framework that systematically unifies computer-aided design, simulation, and measurement data in an adaptive manner. Central to this framework is a reverse engineering algorithm that reconstructs and transforms the geometry of deep-drawn components from optical scan data into B-spline surfaces. The algorithm demonstrated high precision, indicating its suitability for process control and geometric analysis. The hybrid twin framework integrates virtual data from simulations and real-world data, as evidenced by a sensor concept for inline surface measurement. The framework ensures robust and redundant measurement concepts by estimating complete geometries from a few systematically preselected measuring points. This adaptive approach permits continuous updates and extensions to the database, accommodating both sparse inline signals and offline inspection data. This framework provides a conceptual model for integrating direct feedback interactions between virtual and physical environments, thereby enhancing the precision of analytical and predictive models in sheet metal forming processes. [ABSTRACT FROM AUTHOR]

Full-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP's complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures. [ABSTRACT FROM AUTHOR]

The fact that sensor nodes are situated in specific places is most obvious in wireless sensor networks. A wireless sensor network has been designed to address node localization through several solutions. Environmental monitoring, healthcare, and industrial automation are just some of the fields where wireless sensor networks (WSNs) find extensive application. This sort of WSN application is fundamentally dependent on the accurate localization of sensor nodes. The purpose of this paper is to propose an enhanced received signal strength indicator (RSSI) technique for determining unlocalized nodes using anchor nodes. An optimal communication range, aligning with the application needs, can be determined by assessing the dynamic correlation between communication distance and the RSSI throughout the localization procedure. The advanced RSSI-based node localization method proposed in this study involves two distinct stages: the distance determination phase and the computation phase. In this work, we assess the outcome using the Castalia simulation. The precision of wireless sensor network node location is greatly improved by integrating mobile anchor nodes with RSSI-based dynamic node localization. The results of the simulations and the subsequent analysis attest to the excellent accuracy and low energy consumption of the suggested method during the localization procedure. [ABSTRACT FROM AUTHOR]

Forest fires are the source of countless fatalities and extreme economic repercussions. The safe evacuation of residents of an area affected by forest fires is the highest priority of local authorities, and finding the most optimal course of action has been a primary research focus for years. Previous studies over several decades have attempted to find an optimal solution using the applications of bug navigation systems, road network reconfiguration, graph traversals, swarm optimization, etc. The author, with the motivation to prevent human casualties at the time of such calamity, presents a novel study which solves the problem in nearly linear time computation, surpassing the performance standards of previous research, and accommodates the unpredictability of the spread of forest fires. This includes a proposal of an algorithm which builds upon the application of Spielman and Teng's Electrical Circuit Approach to solve for maximum flow in a network and implements this with real-time sensor and Global Positioning System input. [ABSTRACT FROM AUTHOR]

Three-dimensional wireless sensor networks (3D WSNs) are communication networks consisting of numerous wireless sensor nodes. Aiming at the node deployment problem of 3D WSNs, a solution based on the two-layer chaotic particle swarm optimization (TCPSO) algorithm is proposed. TCPSO is utilized to optimize the deployment of nodes and improve the spatial coverage of the network. The TCPSO algorithm divides the population into elite and ordinary subpopulations based on nonlinear classification coefficients, adopting different velocity and position update formulas for iterative optimization. TCPSO introduces a decreasing inertia weight based on Logistic chaotic mapping to control the local exploitation of the algorithm. To avoid premature convergence, levy flight strategy is introduced to enhance the global search ability of the algorithm. Simulation experiments are conducted in three-dimensional grid space to verify the ability of TCPSO and particle swarm optimization (PSO) algorithms to solve the node deployment problem of 3D WSNs. Three sets of experiments are conducted with different numbers of nodes, communication radii, and population sizes, respectively. The control variable method is adopted to observe the performance of TCPSO under different conditions. The node deployment solutions proposed by TCPSO in all experiments are significantly superior to those of PSO. The experimental results indicate that the proposed scheme can effectively improve the spatial coverage of 3D WSNs, reduce the cost of network construction, and provide strong support for practical applications. [ABSTRACT FROM AUTHOR]

Summary: Many optimal sensor placement (OSP) techniques have been developed basing on known external loads. However, it is often difficult to obtain excitation measurements. Therefore, the development of OSP under unknown inputs (OSP‐UI) is desirable. In this paper, based on modal Kalman filter (MKF), an OSP‐UI approach (MKF‐OSP‐UI) is proposed for optimally determining the number and locations of multitype sensors with the aim of minimizing the reconstructed responses errors. An MKF‐based approach previously developed by the authors is first employed for estimating multiscale structural responses and unknown loads. Then, an error covariance matrix is defined as a measure of the differences between the reconstructed responses and the corresponding actual ones. By using the covariance matrix of measurement noise for normalization, the ill‐conditioning problem caused by data fusion of multiscale responses is avoided. The sensors that have few contributions to the reconstructed responses are removed from the candidate set during iteration procedure. The sensor placement is finally determined when the estimation errors are below the preset level. Numerical results show that the sensor configuration determined by the proposed approach has a better performance on the joint estimation of multiscale responses and unknown inputs, as compared with that determined by experience. [ABSTRACT FROM AUTHOR]

Summary The sensor networks are the primary and essential components on which the world of Internet of Things (IoT) is built. IoT empowers smart communication, computation, and sensing capabilities. In sensor networks, the data are collected by the sensor nodes and sent to the sink along a communication path. These communication paths are collaboratively established by the nodes and the sink. By incorporating energy‐efficient data gathering techniques, the lifetime of these networks is improved. The major contribution of the study in this work is to provide a survey of various techniques for data aggregation (DA) and the employed algorithmic strategies that facilitate and influence network lifetime (NL) in these environments. DA in wireless sensor networks (WSN), IoTs, and cloud computing extend the lifetime of these networks since it enables efficient merging of traffic flows, thus reducing transmissions and energy consumption of devices. In sensor networks, data aggregation tree (DAT)‐based routing facilitates energy‐efficient routing that extends NL. NL maximization using DATs constructs DATs with optimal NL and is a known NP‐complete problem. Subsequently, the study in this work surveys the various approaches employed by researchers to construct DATs and discusses techniques for DAT scheduling. This work further explores various sensor deployment techniques and discusses real world scenario in which NL is influenced by uncertainty in communication links. Finally, the study in this survey highlights the achievements in realizing NL improvement using DAT and identifies the limitations and research challenges. [ABSTRACT FROM AUTHOR]

Summary The base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well‐known NP‐hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above‐mentioned problem is modeled by covering a new 2‐D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI‐SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI‐SLOB scheme over the other state‐of‐the‐art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation in which acoustic emission (AE) wave was generated through a pencil lead break as a point source on two pipeline sections made of mild steel and titanium. The pipelines (bare, epoxy phenolic coated) were of same length but had two different diameters and wall thicknesses. The recorded AE signals were analysed using time and frequency domain signals, energy levels and wavelet transform to explore time-frequency features for the identification of wave modes. It is concluded that the damping behaviour of coating restricts the peaks of waves, decreases the decay time of waves and reduces the energy level in the coated pipeline. It is concluded that monitoring of coated pipeline wave propagation could be done effectively with sensor placement directly on the pipeline surface compared to sensor placement on the coating surface. A high wall thickness of pipeline results in a higher number of reflected waves and increases the decay time of waves. The coating on pipeline resists wave propagation, and different densities of coating layer and pipeline affect the velocity of the wave. This experimental work advance towards designing an alternative way to monitor changes in pipeline structures (e.g., corrosion under insulation applications). [ABSTRACT FROM AUTHOR]

The QUANT study represents the most extensive open-access evaluation of commercial air quality sensor systems to date. This comprehensive study assessed 49 systems from 14 manufacturers across three urban sites in the UK over a three-year period. The resulting open-access dataset captures high time-resolution measurements of a variety of gasses (NO, NO2, O3, CO, CO2), particulate matter (PM1, PM2.5, PM10), and key meteorological parameters (humidity, temperature, atmospheric pressure). The quality and scope of the dataset is enhanced by reference monitors' data and calibrated products from sensor manufacturers across the three sites. This publicly accessible dataset serves as a robust and transparent resource that details the methods used for data collection and procedures to ensure dataset integrity. It provides a valuable tool for a wide range of stakeholders to analyze the performance of air quality sensors in real-world settings. Policymakers can leverage this data to refine sensor deployment guidelines and develop standardized protocols, while manufacturers can utilize it as a benchmark for technological innovation and product certification. Moreover, the dataset has supported the development of a UK code of practice, and the certification of one of the participating companies, underscoring the dataset's utility and reliability. [ABSTRACT FROM AUTHOR]

The estimation sound fields over space is of interest in sound field control and analysis, spatial audio, room acoustics and virtual reality. Sound fields can be estimated from a number of measurements distributed over space yet this remains a challenging problem due to the large experimental effort required. In this work we investigate sensor distributions that are optimal to estimate sound fields. Such optimization is valuable as it can greatly reduce the number of measurements required. The sensor positions are optimized with respect to the parameters describing a sound field, or the pressure reconstructed at the area of interest, by finding the positions that minimize the Bayesian Cramér-Rao bound (BCRB). The optimized distributions are investigated in a numerical study as well as with measured room impulse responses. We observe a reduction in the number of measurements of approximately 50% when the sensor positions are optimized for reconstructing the sound field when compared with random distributions. The results indicate that optimizing the sensors positions is also valuable when the vector of parameters is sparse, specially compared with random sensor distributions, which are often adopted in sparse array processing in acoustics. [ABSTRACT FROM AUTHOR]

Oxygen uptake ( V ˙ O 2 ) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate ( H R ) is a prominent physiological indicator correlated with V ˙ O 2 and is often used for indirect V ˙ O 2 prediction. This study investigates the impact of H R placement on V ˙ O 2 prediction accuracy by analyzing H R data combined with the respiratory rate ( R E S P ) and minute ventilation ( V ˙ E ) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V ˙ O 2 , R E S P , V ˙ E , and H R were collected and used to develop a neural network model for V ˙ O 2 prediction. The influence of H R position on prediction accuracy was assessed via Bland–Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that H R combined with R E S P and V ˙ E ( V ˙ O 2 H R + R E S P + V ˙ E ) produces the most accurate V ˙ O 2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V ˙ O 2 prediction decreases, particularly within high-intensity exercise. The substitution of H R with different anatomical sites significantly impacts V ˙ O 2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering H R placement in V ˙ O 2 prediction models, with R E S P and V ˙ E serving as effective compensatory factors. These findings contribute to refining indirect V ˙ O 2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements. [ABSTRACT FROM AUTHOR]

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant research directed toward the maximization of k-submodular functions, owing to their wide-ranging applications in fields such as social networks, sensor placement, and various other domains. The utilization of the greedy method and randomized techniques is prevalent for the design of algorithms that offer favorable performance guarantees for such problems. However, these approaches adopt a single-item selection strategy at each iteration, leading to high query complexity. The Threshold-Decreasing Algorithm is conceptualized to address this issue by choosing multiple items using a predefined threshold value, and in case no items remain, the threshold is decremented by a specified factor. This approach effectively reduces complexity. In this paper, we merge the principles of the threshold-decreasing and greedy methods to enhance the complexity of constrained k-submodular maximization problems, thereby achieving near-optimal approximation ratios. [ABSTRACT FROM AUTHOR]

Magneto-oscillatory devices have been recently developed as very potent wireless miniature position trackers and sensors with an exceptional accuracy and sensing distance for surgical and robotic applications. However, it is still unclear to which extend a mechanically resonating sub-millimeter magnet interacts with external magnetic fields or gradients, which induce frequency shifts of sub-mHz to several Hz and, therefore, affect the sensing accuracy. Here, we investigate this effect experimentally on a cantilever-based magneto-oscillatory wireless sensor (MOWS) and build an analytical model concerning magnetic and mechanical interactions. The millimeter-scale MOWS is capable to detect magnetic fields with sub-µT resolution to at least ±5 mT and simultaneously detects magnetic field gradients with a resolution of 65 µT/m to at least ±50 mT/m. The magnetic field sensitivity allows direct calculation of mechanical device properties, and by rotation, individual contributions of the magnetic field and gradient can be analyzed. The derived model is general and can be applied to other magneto-oscillatory systems interacting with magnetic environments. [ABSTRACT FROM AUTHOR]

This paper deals with the investigation of factors influencing the movement patterns of a reactivated slow-moving landslide situated in the eastern Liguria region (NW Italy) through the analysis of extensive ground-based hydrological and geotechnical monitoring data. Subsurface horizontal displacement and pore water pressure data were acquired simultaneously by means of automatic sensors positioned at pre-existing and localized failure zones. The joint examination of field measurements enabled us to explore the connections between rain, pore water pressure, and displacements. The results of continuous displacement monitoring showed that the landslide kinematics involved phases of extremely slow movements alternated with periods of relative inactivity. Both stages occurred prevalently at seasonal scale displaying similar durations. The slow-motion phases took place at relatively constant pore water pressure and were ascribed to mechanisms of viscous shear displacements along failure surfaces. Inactive phases entailed no significant deformations, mostly corresponding to prolonged dry periods. The two motion patterns were interrupted by episodic sharp deformations triggered by delayed (preparation periods from 4 to 11 days) rainfall-induced pore water pressure peaks, which were ascribed to sliding mechanisms taking place through rigid-plastic frictional behaviour. During these deformation events, hysteresis relationships between pore water pressure and displacement were found, revealing far more complex hydro-mechanical behaviour. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

Robotic platforms including Unmanned Aerial Vehicles (UAVs) require an accurate and reliable source of position information, especially in indoor environments where GNSS cannot be used. This is typically accomplished by using multiple independent position sensors. This paper presents a UAV position estimation mechanism based on a particle filter, that combines information from visual odometry cameras and visual detection of fiducial markers. The article proposes very compact, lightweight and robust method for indoor localization, that can run with high frequency on the UAV's onboard computer. The filter is implemented such that it can seamlessly handle sensor failures and disconnections. Moreover, the filter can be extended to include inputs from additional sensors. The implemented approach is validated on data from real-life UAV test flights, where average position error under 0.4 m was achieved. [ABSTRACT FROM AUTHOR]

The objective was to compare simplified pressure insoles integrating different sensor numbers and to identify a promising range of sensor numbers for accurate center of pressure (CoP) measurement. Twelve participants wore a 99-sensor Pedar-X insole (100 Hz) during walking, jogging, and running. Eight simplified layouts were simulated, integrating 3–17 sensors. Concordance correlation coefficients (CCC) and root mean square errors (RMSE) between the original and simplified layouts were calculated for time-series mediolateral (ML) and anteroposterior (AP) CoP. Differences between layouts and between gait types were assessed via ANOVA and Friedman test. Concordance between the original and simplified layouts varied across layouts and gaits (CCC: 0.43–0.98; χ (7) 2 ≥ 34.94, p < 0.001). RMSEML and RMSEAP [mm], respectively, were smaller in jogging (5 ± 2, 15 ± 9) than in walking (8 ± 2, 22 ± 4) and running (7 ± 4, 20 ± 7) ( η p 2 : 0.70–0.83, p < 0.05). Only layouts with 11+ sensors achieved CCC ≥ 0.80 in all tests across gaits. The 13-sensor layout achieved CCC ≥ 0.95 with 95% confidence, representing the most promising compromise between sensor number and CoP accuracy. Future research may refine sensor placement, suggesting the use of 11–13 sensors. For coaches, therapists, and applied sports scientists, caution is recommended when using insoles with nine or fewer sensors. Consulting task-specific validation results for the intended products is advisable. [ABSTRACT FROM AUTHOR]

This paper expands a recently proposed peak current-mode (PCM) control method for a power factor correction (PFC) boost converter to include the totem-pole converter and solves the controller's compatibility problem with the totem-pole converter by proposing three input current sensing methods. Using MATLAB/Simulink 2023b, simulation experiments on a 2 kW totem-pole converter utilizing the PFC PCM controller were carried out to assess the performance of the controller with the proposed sensing methods. The findings indicate that under steady-state conditions, all three proposed sensing methods performed input current shaping successfully and yielded nearly identical THD% of about 4.4% in the input current waveform. However, it is noteworthy that method 2, referred to as the memory method, exhibited a sluggish and less robust transient response in comparison to the swift and resilient responses observed with method 1 and method 3. Additionally, the third proposed method, which involves a single current sensor positioned across the input inductor, emerged as the optimal and cost-effective sensing solution. This method achieved the same desirable attributes of fast and robust control while utilizing only a single current sensor, a notable advantage over method 1, which employs two current sensors. [ABSTRACT FROM AUTHOR]

We present a method for improving the amplitude and angular error of inductive position sensors, by advancing the design of receiver coil systems with multiple windings on two layers of a printed circuit board. Multiple phase-shifted windings are connected in series, resulting in an increased amplitude of the induced voltage while decreasing the angular error of the sensor. The amplitude increase for a specific number of windings can be predicted in closed form. Windings are placed electrically in series by means of a differential connection structure, without adversely affecting the signal quality while requiring a minimal amount of space in the layout. Further, we introduce a receiver coil centerline function which specifically enables dense, space-constrained designs. It allows for maximization of the number of possible coil windings while minimizing the impact on angular error. This compromise can be fine-tuned freely with a shape parameter. The application to a typical rotary encoder design for motor control applications with five periods is presented as an example and analyzed in detail by 3D finite-element simulation of 18 different variants, varying both the number of windings and the type of centerline functions. The best peak-to-peak angular error achieved in the examples is smaller than 0.1° electrically (0.02° mechanically, periodicity 5) under nominal tolerance conditions, in addition to an amplitude increase of more than 170% compared to a conventional design which exhibits more than twice the angular error. Amplitude gains of more than 270% are achieved at the expense of increased angular error. [ABSTRACT FROM AUTHOR]

We propose a hands-free control system for a human-guided smart stroller. The proposed method uses real-time peer-to-peer localization technology of the human and stroller to realize an intuitive hands-free control system based on the relative position between the human and the stroller. The control method is also based on functional and mechanical safety to ensure the safety of the stroller's occupant (child) and the pilot (parent) during locomotion. In this paper, first, we present a preliminary investigation of the humans' preference for the relative position in the context of hands-free guided strollers. Then, we present the control method and a prototype implemented with an electric wheelchair and UWB sensors for localization. We present an experimental evaluation of the proposed method with 14 persons walking with the developed prototype to investigate the usability and soundness of the proposed method compared to a remote joystick and manual operation. The evaluation experiments were conducted in an indoor environment and revealed that the proposed method matches the performance of joystick control but does not perform as well as manual operation. Notably, for female participants, the proposed method significantly surpasses joystick performance and achieves parity with manual operation, which shows its efficacy and potential for a smart stroller. Also, the results revealed that the proposed method significantly decreased the user's physical load compared to the manual operation. We present discussions on the controllability, usability, task load, and safety features of the proposed method, and conclude this work with a summary assessment. [ABSTRACT FROM AUTHOR]

The precise thermal error prediction of spindle-bearing systems (SBSs) necessitates a comprehensive analysis of information gathered from multi-source sensors. However, limited data availability due to structural constraints poses challenges to fully characterize the system state. In this study, we introduce a data-model hybrid-driven framework based on sensor optimization placement for accurate thermal error prediction of SBSs. Firstly, a thermal hypernetwork method is developed to consider uneven temperature distribution and establish a unified information fusion model for state estimation. Secondly, based on an analysis of the rapidity and robustness, robust geodesic distance-based fuzzy c-medoid clustering with a simulated annealing algorithm (RGDFCMSA) is proposed to optimize sensor placement by minimizing the information entropy of the system. Next, uncertain parameters with estimability are selected based on SIAN and Sobol's sensitivity indicator under optimal sensor placement. Furthermore, a multilayer particle filter (MLPF) is proposed to estimate temperature fields and predict the thermal error of SBSs by fusing information from multiple sources with different fidelity. Finally, experiments under different working conditions are conducted to validate the effectiveness and accuracy of the proposed method. The result indicates that the proposed framework is capable of an accurate estimation of the global temperature field, uncertain thermal parameters and thermal errors. • A thermal hypernetwork method is proposed for unified information fusion. • A network planning algorithm RGDFCMSA is proposed for optimizing sensor layout. • MLPF is proposed for multi-fidelity and multi-physics sensor fusion. • A hybrid-driven framework is proposed for thermal error prediction. [ABSTRACT FROM AUTHOR]

Sleep disturbances following a concussion/mild traumatic brain injury are associated with longer recovery times and more comorbidities. Sensor technologies can directly monitor sleep-related physiology and provide objective sleep metrics. This scoping review determines how sensor technologies are currently used to monitor sleep following a concussion. We searched Ovid (Medline, Embase), Web of Science, CINAHL, Compendex Engineering Village, and PsycINFO from inception to June 20, 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Included studies objectively monitored sleep in participants with concussion. We screened 1081 articles and included 37 in the review. A total of 17 studies implemented polysomnography (PSG) months to years after injury for a median of two nights and provided a wide range of sleep metrics, including sleep–wake times, sleep stages, arousal indices, and periodic limb movements. Twenty-two studies used actigraphy days to weeks after injury for a median of 10 days and nights and provided information limited to sleep–wake times. Sleep stages were most reported in PSG studies, and sleep efficiency was most reported in actigraphy studies. For both technologies there was high variability in reported outcome measures. Sleep sensing technologies may be used to identify how sleep affects concussion recovery. However, high variability in sensor deployment methodologies makes cross-study comparisons difficult and highlights the need for standardization. Consensus on how sleep sensing technologies are used post-concussion may lead to clinical integration with subjective methods for improved sleep monitoring during the recovery period. [ABSTRACT FROM AUTHOR]

Summary: In the last decades, wireless sensor networks (WSNs) have appeared as an active research area owing to their high applicability in different application areas like healthcare, automation, and the military. Several issues have been encountered in the WSNs, including energy consumption, deployment of sensor nodes, routing algorithms, energy efficiency, cluster head (CH) selection, and robustness. Cluster‐based routing is one of the energy‐efficient solutions in the WSN. At the same time, the hot spot problem remains a challenging problem, which needs to be addressed in the design of energy‐efficient solutions for WSNs. Motivated by the abovementioned discussion, c The comprehensive comparison study pointed out the betterment of the FLHBA‐CMHR technique in terms of different measures. The FLHBA‐CMHR technique obtains a maximum half‐node die (HND) of 1629 nodes, while the LEACH, FUCA, URBD, DEFL, and E‐FUCA techniques attain decreased HND of 793, 1085, 1092, 1139, and 1344 nodes correspondingly. [ABSTRACT FROM AUTHOR]

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node's estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost. [ABSTRACT FROM AUTHOR]

Measuring the dynamic displacements of a structure provides a comprehensive understanding of the structure, especially when subjected to different types of dynamic loading (i.e., wind, traffic, impact loads, blast loads, etc.). Despite their usefulness, direct displacement measurements are typically not collected due to the cumbersome logistical issues of sensor placement and maintenance and the impracticality of instrumenting contact-based sensors across all significant structures. In this context, this study proposes a novel dual stereovision technique to measure the dynamic displacement of structures using a portable, noncontact measurement system that involves an uncrewed aerial system (UAS) and four optical cameras. One pair of cameras tracks the three-component (x , y , and z) motion of a region of interest (ROI) on a structure with respect to the UAS system, and the other pair of cameras measure the six degrees of freedom motion (6-DOF) (both rotational and translational motion) of the UAS system by tracking a stationary reference. The motion of the UAS is then compensated for to recover the true dynamic displacement of the ROI. The proposed dual stereovision technique realizes simultaneous measurement of all three components of displacements of the structure and 6-DOF of UAS motion through a mathematically elegant process. The unique dual stereovision technique allows flexibility in choosing a global reference coordinate system, greatly enhancing the feasibility of applying the new technology in various field environments. This new technique has overcome the major challenge of significant UAS motions in full-scale applications. Furthermore, this technique relies on natural features and eliminates the requirement of artificial targets on the structure, permitting applications to difficult-to-access structures. [ABSTRACT FROM AUTHOR]

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and mapping. This article aims to provide an overview of recent progress and advancements in LiDAR-based global localization. We begin by formulating the problem and exploring the application scope. We then present a review of the methodology, including recent advancements in several topics, such as maps, descriptor extraction, and cross-robot localization. The contents of the article are organized under three themes. The first theme concerns the combination of global place retrieval and local pose estimation. The second theme is upgrading single-shot measurements to sequential ones for sequential global localization. Finally, the third theme focuses on extending single-robot global localization to cross-robot localization in multi-robot systems. We conclude the survey with a discussion of open challenges and promising directions in global LiDAR localization. To our best knowledge, this is the first comprehensive survey on global LiDAR localization for mobile robots. [ABSTRACT FROM AUTHOR]

Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver's field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver's level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including well-placed charging stations (CS), is critical to enhancing connectivity. To overcome this, consumers want real-time data on charging station availability, neighboring station locations, and access times. This work leverages the Distance Vector Multicast Routing Protocol (DVMRP) to enhance the information collection process for charging stations through the Internet of Things (IoT). The evolving IoT paradigm enables the use of sensors and data transfer to give real-time information. Strategic sensor placement helps forecast server access to neighboring stations, optimize vehicle scheduling, and estimate wait times. A recommender system is designed to identify stations with more rapidly charging rates, along with uniform pricing. In addition, the routing protocol has a privacy protection strategy to prevent unauthorized access and safeguard EV data during exchanges between charging stations and user locations. The system is simulated with MATLAB 2020a, and the data are controlled and secured in the cloud. The predicted algorithm's performance is evaluated using several kinds of standards, including power costs, vehicle counts, charging costs, energy consumption, and optimization values. [ABSTRACT FROM AUTHOR]

New Adaptive Nano-Scale Sensor Network (ANSN) can quickly feel nanoscale surroundings. ANSN uses data in many scenarios to improve networks, consume less energy, and gain more accurate data. ANSI essentials are covered in detail here. This group has numerous parts. Making service better, collecting data with less energy, sending data with Q-learning, merging sensor data to increase accuracy, controlling power dynamically, and protecting data using AES are examples. Energy collection and sensor use are key to this effort. Academic research has proven that ANSN outperforms other techniques in several areas. Improvements include speed, security, latency, sensor accuracy, and network stability. With these changes, ANSN may be suitable for small wireless sensor networks [ABSTRACT FROM AUTHOR]

Localization is an extremely important component of applications that make use of wireless sensor networks. It has a substantial impact on academics as well as real-time sensor deployment applications in the aim of lowering the amount of energy that is used while simultaneously locating unknown nodes. The process of obtaining the coordinates along an axis that represent the locations of the sensor nodes is referred to as localization. The accuracy of locating the positions of the nodes varies depending on the environmental conditions, the type of nodes, the type of application, and the type of localization methods used. A standard localization method known as distance vector hop (DV-hop) localization will be able to determine the positions of unknown nodes with typical accuracy with the assistance of beacon nodes based on Internet of things. The DV-hop and improved weighted centroid localization algorithms, in addition to the suggested boosted weight centroid-based localization approach, are both addressed in this article. The suggested boosted weight centroid localization technique is utilized to find nodes in the remote area of the WSN while conserving energy. This is accomplished with the assistance of measurements involving both the nodes and the centroid. The modified weight metric is utilized in the process of carrying out the task of localisation of an unknown node. The performance of BWCLA is evaluated based on a number of different metrics, including accuracy in localization, average localization error, total packets utilized, and energy usage. [ABSTRACT FROM AUTHOR]

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities. [ABSTRACT FROM AUTHOR]

This study proposes a high-resolution rotation-measuring system for miniaturized MEMS ultrasonic motors using tunneling magnetoresistance (TMR) sensors for the first time. Initially, the architecture and principle of the rotation-measuring system are described in detail. Then, the finite element simulation is implemented to determine the miniaturized permanent magnet's residual magnetization, dimensions, and TMR sensor position. Finally, the experiments are implemented to evaluate the performance. Using calibration based on a high-precision servo motor, it is found that the relationship between the output and rotational angle is highly linear and immune to the rotor's out-of-plane movement. Meanwhile, the angle-detecting resolution is higher than 0.1°. After the calibration, the continuous rotation of the MEMS ultrasonic motor is tested. It is found that the angle testing result varies with a period close to 360°, which indicates that the rotation-measuring system has successfully detected the motor's rotation. [ABSTRACT FROM AUTHOR]

This paper presents the design, simulation, fabrication, and characterization of a novel large-scan-range electrothermal micromirror integrated with a pair of position sensors. Note that the micromirror and the sensors can be manufactured within a single MEMS process flow. Thanks to the precise control of the fabrication of the grid-based large-size Al/SiO2 bimorph actuators, the maximum piston displacement and optical scan angle of the micromirror reach 370 μm and 36° at only 6 Vdc, respectively. Furthermore, the working principle of the sensors is deeply investigated, where the motion of the micromirror is reflected by monitoring the temperature variation-induced resistance change of the thermistors on the substrate during the synchronous movement of the mirror plate and the heaters. The results show that the full-range motion of the micromirror can be recognized by the sensors with sensitivities of 0.3 mV/μm in the piston displacement sensing and 2.1 mV/° in the tip-tilt sensing, respectively. The demonstrated large-scan-range micromirror that can be monitored by position sensors has a promising prospect for the MEMS Fourier transform spectrometers (FTS) systems. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs) emphasise the importance of clean air (SDG 3 and SDG 11.6). Japan faces challenges in air quality prediction, especially in rural areas, because of the absence of a climate-specific calibration model and limited deployment of low-cost sensors. Our study compared three sensor types: low-cost pre-assembled and pre-calibrated sensors (PurpleAir); self-assembled low-cost sensors (Citizen Science Project); and highly accurate weather stations, focusing on PM2.5 values. Linear and generalised linear mixed models show potential for low-cost sensor calibration. [ABSTRACT FROM AUTHOR]

Directional sensor networks are a widely used architecture in the sensing layer of the Internet of Things (IoT), which has excellent data collection and transmission capabilities. The coverage hole caused by random deployment of sensors is the main factor restricting the quality of data collection in the IoT sensing layer. Determining how to enhance coverage performance by repairing coverage holes is a very challenging task. To this end, we propose a node deployment optimization method to enhance the coverage performance of the IoT sensing layer. Firstly, with the goal of maximizing the effective coverage area, an improved particle swarm optimization (IPSO) algorithm is used to solve and obtain the optimal set of sensing directions. Secondly, we propose a repair path search method based on the improved sparrow search algorithm (ISSA), using the minimum exposure path (MEP) found as the repair path. Finally, a node scheduling algorithm is designed based on MEP to determine the optimal deployment location of mobile nodes and achieve coverage enhancement. The simulation results show that compared with existing algorithms, the proposed node deployment optimization method can significantly improve the coverage rate of the IoT sensing layer and reduce energy consumption during the redeployment process. [ABSTRACT FROM AUTHOR]

The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm's localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland. [ABSTRACT FROM AUTHOR]

In structural health monitoring, because the number of sensors used is far lower than the number of degrees of freedom of the structure being monitored, the optimization problem of the location and number of sensors in the structures is becoming more and more prominent. However, spatial grid structures are complex and diverse, and their dynamic characteristics are complex. It is difficult to accurately measure their vibration information. Therefore, an appropriate optimization method must be used to determine the optimal positioning of sensor placement. Aiming at the problem that spatial grid structures have many degrees of freedom and the fact that it is difficult to obtain complete vibration information, this paper analyzed the typical EI method, MKE method, and EI-MKE method in the arrangement of the measuring points, and it was verified that the EI method was more suitable for the vibration detection of spatial grid structures through the example of a plane truss and spatial grid structures. Measuring points under the assumption of structural damage were explored, and it was proposed that there might have been a stable number of measuring points that could cover the possible vibration mode changes in the structures. At the same time, combined with the three-level improved Guyan recursive technique, in order to obtain better complete modal parameters, the influence of the number of measuring points on the complete vibration mode information was studied. It was concluded that MACd was better than MACn as the quantitative target. [ABSTRACT FROM AUTHOR]

In this study, we implemented a compact wind direction sensor on a palm-sized quadcopter to achieve wind source localization (WSL). We designed an anemotaxis algorithm based on the sensor data and experimentally validated its efficacy. Anemotaxis refers to the strategy of moving upwind based on information on the wind direction, which is essential for tracing odors propagating through the air. Despite the limited research on quadcopter systems achieving WSL directly through environmental wind measurement sensors, debate remains regarding the relationship between sensor placement and the anemotaxis algorithm. Therefore, we experimentally investigated the placement of a wind direction sensor capable of estimating wind source direction even when propellers are rotating. Our findings demonstrated that placing the sensor 50 mm away from the enclosure of the quadcopter allowed accurate wind direction measurement without being affected by wake disturbances. Additionally, we constructed an anemotaxis algorithm based on wind direction and speed data, which we integrated into the quadcopter system. We confirmed the ability of the quadcopter to execute anemotaxis behavior and achieve WSL irrespective of environmental wind strength through wind source localization experiments. [ABSTRACT FROM AUTHOR]

Temperature field measurements are of great significance in industrial production, scientific research, and other fields. Contact temperature measurements usually achieve high spatial resolution by increasing the layout density of the sensors. However, in practical applications, achieving high-density sensor placement is often difficult. Therefore, when the number of sensors is limited, it is necessary to perform function fitting or interpolation of the temperature of the sampling points to reconstruct the temperature field distribution. This study proposed a temperature field reconstruction method based on femtosecond laser prepared multiwavelength fibre Bragg grating (FBG) array. A multiwavelength FBG array was prepared using the femtosecond laser phase mask method combined with stress stretching, and applied for measuring the temperature field distribution in a tubular high-temperature furnace. Cubic spline interpolation and backpropagation (BP) neural networks were used to construct two-dimensional temperature field models for the temperature field distribution data measured by the FBGs, and the prediction accuracies of the two models were compared. The test results show that the root mean square error of the temperature field distribution constructed using the BP neural network is 0.7333 °C, which is approximately 23.18% of the predicted results of the cubic spline interpolation model, indicating that it is a high-precision temperature field reconstruction method. [ABSTRACT FROM AUTHOR]

Bursts in water distribution systems (WDSs) lead to water wastage and negative environmental impacts. Optimizing pressure sensor placement (PSP) for effective burst detection is crucial for prompt response and adverse event mitigation. While many optimization methods are available for this purpose, their resulting PSP strategies often exhibit large variability caused by background noise or metering accuracy, representing low robustness. This consequently hinders the wide application of existing PSP methods for burst detection in WDSs. This study proposes a novel multi‐objective PSP method aimed at maximizing burst detection while minimizing the number of sensors. Within this method, a new threshold metric is introduced to quantify the minimum detectable burst outflow across all pipes. The metric has the key advantage of taking into account the fact that the relative magnitude of the detectable threshold for different pipes is independent of noise, thereby addressing the low robustness issue. The proposed method is applied to three WDSs, and results show that it can consistently achieve robust optimal PSP strategies across diverse noise conditions. Comprehensive comparisons between the proposed method and the other benchmark approaches are conducted using five metrics, including detection performance and layout characteristics. These comparisons reveal the properties of each optimization method and show how detection performance is affected by various placement features. It is anticipated that the proposed method can be useful in engineering practice due to its great robustness in determining the optimal PSP strategies for burst detection in WDSs compared to other alternatives. Plain Language Summary: In water distribution systems (WDSs), bursts can cause water wastage and environmental harm. Strategic pressure sensor placement (PSP) is crucial for prompt burst detection and mitigation. However, existing PSP methods often lack robustness due to background noise or metering inaccuracies, hindering their widespread use. This study introduces a novel multi‐objective PSP method aiming to maximize burst detection while minimizing sensor count. It includes a new threshold metric to quantify minimum detectable burst outflow, independent of noise, addressing the robustness issue. Based on results from three case studies, the proposed method consistently achieves optimal PSP strategies across diverse noise conditions. Comparative analyses with benchmark approaches demonstrate its robustness and effectiveness in detecting bursts. This method can be very useful in engineering as it can provide reliable PSP strategies for WDS burst detection. Key Points: The proposed method deals with issues of low robustness and inconsistency within pressure sensor placementsFive metrics and three case studies are used for comparison between the proposed method and three existing approachesPlacements with a larger average shortest‐path distance outperform those with an average geographic‐plane distance in detection performance [ABSTRACT FROM AUTHOR]

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