Autonomous Data Acquisition Research Articles

MAM-STM: A software for autonomous control of single moieties towards specific surface positions

In this publication we introduce MAM-STM, a software to autonomously manipulate arbitrary moieties towards specific positions on a metal surface utilizing the tip of a scanning tunneling microscope (STM). Finding the optimal manipulation parameters for a specific moiety is challenging and time consuming, even for human experts. MAM-STM combines autonomous data acquisition with a sophisticated Q-learning implementation to determine the optimal bias voltage, the z-approach distance, and the tip position relative to the moiety. This then allows to arrange single molecules and atoms at will. In this work, we provide a tutorial based on a simulated response to offer a comprehensive explanation on how to use and customize MAM-STM. Additionally, we assess the performance of the machine learning algorithm by benchmarking it within a simulated stochastic environment. PROGRAM SUMMARYProgram title: MAM-STMCPC Library link to program files: https://doi.org/10.17632/gtf3bt4v47.1Developer's repository link: https://gitlab.tugraz.at/software_public/mam_stm.gitLicensing provisions: GNU General Public License 3 (GPL)Programming language: Python 3Nature of problem: Achieving precise control over the arrangement of individual molecules on surfaces is essential for advancing nanofabrication and understanding molecular interaction processes. While self-assembly offers a method for forming nanostructures, achieving arbitrary arrangements of moieties remains difficult. Current approaches, such as scanning probe microscopy (SPM), require extensive manual intervention and precise control is difficult to achieve consistently due to the stochastic nature of quantum mechanical systems at the nanoscale. Thus, learning to manipulate several moieties in order to build even relatively small structures is challenging and time consuming and the automation through conventional expert systems is hindered by the lack of prior knowledge about the surface-moiety interaction processes.Solution method: This scenario is ideal for machine learning algorithms, like reinforcement learning (RL), which do not require an underlying model but are able to autonomously learn the optimal manipulation parameters by performing manipulations directly at the machine. Introducing MAM-STM, which stands for Molecular and Atomic Manipulation via Scanning Tunneling Microscopy. MAM-STM allows to control molecules and atoms by learning the manipulation parameters for either vertical or lateral manipulations. However, the vast number of manipulation parameter combinations and the inefficient learning procedure of RL agents exhibit several challenges. MAM-STM overcomes these challenges with an autonomous masking routine that eliminates manipulation parameters that induce structural changes to the moiety or lift it off the surface. Additionally, a sophisticated Q-learning approach is developed that speeds up the learning procedure, enabling molecular manipulations within one day of training.

An Autonomous Data Acquisition and System Identification Framework for In-Service Wind Turbine Using p-LSCF Method

An Autonomous Data Acquisition and System Identification Framework for In-Service Wind Turbine Using p-LSCF Method

MATLAB-based simulation of industrial robots in water environment monitoring

Abstract The use of industrial robots based on MATLAB simulation for water environment monitoring is to monitor the water environment better, improve monitoring efficiency and reduce monitoring costs. The robot can better collect data and can engage in deeper water-specific information. In this paper, based on the discussion of the water environment monitoring robots used in countries around the world for water environment monitoring, we introduce a MATLAB-based simulation of industrial robots in a wide range of water environments to simulate the autonomous data acquisition system. The main advantages are: compared with other robots, it can realize the “wide range” of water environment data collection; compared with fixed buoys, it can realize the “autonomous” collection of water environment monitoring data and gives the autonomous collection process and hierarchical software progression. The autonomous acquisition process and hierarchical software architecture are presented. The simulation results analysis shows no difference between the simulated data and the predicted data from the historical data using MATLAB-based industrial robots for water environment monitoring. This shows that the development of industrial robot simulation in water environment monitoring is promising and feasible.

Comparison of deep learning and analytic image processing methods for autonomous inspection of railway bolts and clips

In this work, different methods are proposed and compared for autonomous inspection of railway bolts and clips. A prototype of an autonomous data acquisition system was developed to automatically obtain information of the state of the railway track using LiDAR and camera sensors. This system was employed in a testing railway track installed in the facilities of the University of Vigo to obtain the images used in this work. Then, the images were further processed using analytic image segmentation algorithms as well as a neural network to detect the bolts and clips. Once these elements are detected, their relative position is computed to evaluate if there is any missing component. Finally, the orientation of the clips is computed to ensure that all the bolts are correctly placed. Four different methods were implemented, and their performance was evaluated using the segmentations provided by the analytical methods and the neural network.

ET-AL: Entropy-targeted active learning for bias mitigation in materials data

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.

A Review on Viewpoints and Path Planning for UAV-Based 3-D Reconstruction

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This review paper investigates a wide range of model-free and model-based algorithms for viewpoint and path planning for 3D reconstruction of large-scale objects. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3D reconstruction purposes. In addition to discussing the evaluation strategies, this paper also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.

Neuro-Inspired Autonomous Data Acquisition for Energy-Constrained IoT Sensors

The unprecedented pervasiveness of the Internet of Things (IoT) has unleashed an urgent need for autonomous IoT sensors that do not only autonomously operate, but more importantly autonomously also make intelligent decisions, including when and what data to acquire. Inspired by the autonomous nervous system (ANS) and its rapid de-centralized response to sensory stimuli, this article proposes an autonomous data acquisition approach for energy-constrained IoT sensors. The proposed approach achieves autonomy through rapid real-time event detection in the analog domain, which is then used to instantaneously trigger data acquisition from the sensor, without needing to consult the processor in making such a decision. Accordingly, the analog event-detection circuit would be the only circuit that is continuously ON, while all other system blocks remain in the sleep mode until an event is detected, significantly reducing the operation time of the overall system and the amount of redundant data it produces. A proof-of-concept circuit is designed and implemented, and its performance is verified and analyzed through extensive simulations and experiments, demonstrating event-detection speeds at the order of microseconds; orders of magnitude faster than the required limit for lossless data acquisition in many IoT applications. A case study on an Industrial IoT (IIoT) application is investigated through circuit-level implementation and simulations on real seismic data. The presented results demonstrate the feasibility of lossless autonomous active seismic data acquisition with a 95% reduction in the overall operation time of the sensor node as well as in the amount of data it produces compared to conventional data-acquisition approaches.

Gaussian process-assisted active learning for autonomous data acquisition of impact echo

The goal of this study is to pursue sampling efficiency and achieve reasonable accuracy for the quantity of interest (damage) in impact-echo testing. Because the locations of damages are not always obvious, the conventional grid-based measurement requires subjective judgment. The proposed solution is the implementation of Gaussian process (GP)-assisted active learning to enable the sequential sampling of the quantity of interest with autonomous decision-making for data acquisition. Throughout sampling, an automated model selection is employed to select a suitable kernel for GP modeling dynamically. Active learning is then applied to identify potential damage locations without any human assistance. The proposed method iterates the two modules to explore unknown damage and improve the accuracy of the quantity of interest until the maximum sample size is reached. Through numerical and experimental validation, this work demonstrates that the proposed method exhibits more accurate and efficient results.

Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario.

Mobile crowdsensing (MCS) is a sensing paradigm that allows ordinary citizens to use mobile and wearable technologies and become active observers of their surroundings. MCS services generate a massive amount of data due to the vast number of devices engaging in MCS tasks, and the intrinsic mobility of users can quickly make information obsolete, requiring efficient data processing. Our previous work shows that the Bloom filter (BF) is a promising technique to reduce the quantity of redundant data in a hierarchical edge-based MCS ecosystem, allowing users engaging in MCS tasks to make autonomous informed decisions on whether or not to transmit data. This paper extends the proposed BF algorithm to accept multiple data readings of the same type at an exact location if the MCS task requires such functionality. In addition, we thoroughly evaluate the overall behavior of our approach by taking into account the overhead generated in communication between edge servers and end-user devices on a real-world dataset. Our results indicate that using the proposed algorithm makes it possible to significantly reduce the amount of transmitted data and achieve energy savings up to 62% compared to a baseline approach.

Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities

The execution and analysis of complex experiments are challenged by the vast dimensionality of the underlying parameter spaces. Although an increase in data-acquisition rates should allow broader querying of the parameter space, the complexity of experiments and the subtle dependence of the model function on input parameters remains daunting owing to the sheer number of variables. New strategies for autonomous data acquisition are being developed, with one promising direction being the use of Gaussian process regression (GPR). GPR is a quick, non-parametric and robust approximation and uncertainty quantification method that can be applied directly to autonomous data acquisition. We review GPR-driven autonomous experimentation and illustrate its functionality using real-world examples from large experimental facilities in the USA and France. We introduce the basics of a GPR-driven autonomous loop with a focus on Gaussian processes, and then shift the focus to the infrastructure that needs to be built around GPR to create a closed loop. Finally, the case studies we discuss show that Gaussian-process-based autonomous data acquisition is a widely applicable method that can facilitate the optimal use of instruments and facilities by enabling the efficient acquisition of high-value datasets. Gaussian process regression (GPR) is a powerful, non-parametric and robust technique for uncertainty quantification and function approximation that can be applied to optimal and autonomous data acquisition. This Review introduces the basics of GPR and discusses several use cases from different fields.

Advancing hydrological process understanding from long‐term resistivity monitoring systems

AbstractMonitoring subsurface flow and transport processes over a wide range of spatiotemporal scales remains one of the greatest challenges in hydrology. Electrical geophysical techniques have been implemented to noninvasively investigate a broad range of subsurface hydrological processes. Recent advances in instrumentation and interpretational tools highlight the emerging opportunities to adopt long‐term resistivity monitoring (LTRM) to improve understanding of flow and transport processes operating over monthly to decadal timescales that are not adequately captured in short‐term monitoring data sets and are temporally aliased in data sets constructed from occasional reoccupation of a study site. The emergence of LTRM as a robust tool in hydrology represents a paradigm shift in geophysical data acquisition and analysis, with resistivity monitoring now evolving into a hydrological decision support technology. We describe the theoretical basis for adopting LTRM for noninvasive monitoring of hydrological state variables over multiple spatial scales and with higher temporal resolution than achieved from periodic reoccupation of a field site. Instrumentation developments facilitating autonomous data acquisition at off the grid field sites are discussed, along with advances in data processing that enhance the hydrological information content inherent in LTRM data sets. Case studies from a diverse range of hydrology subdisciplines highlight the largely untapped potential for LTRM to provide information beyond the reach of established hydrology tools. Future opportunities and challenges relating to the more widespread adoption of LTRM, including addressing inherent uncertainty in resistivity interpretation, upscaling, computational, and modeling needs are critically discussed.This article is categorized under: Science of Water (WCAA)

Active learning accelerates ab initio molecular dynamics on reactive energy surfaces

<h2>Summary</h2> Modeling dynamical effects in chemical reactions typically requires <i>ab initio</i> molecular dynamics (AIMD) simulations due to the breakdown of transition state theory (TST). Reactive AIMD simulations are limited to lower-accuracy electronic structure methods and weak statistics because quantum mechanical energies and forces must be evaluated at femtosecond time resolution over many replicas. We report a data-driven pipeline that allows for the treatment of dynamical effects with the same level of theory and overall cost as that of TST approaches. High-throughput <i>ab initio</i> calculations and autonomous data acquisition are coupled to graph convolutional neural-network interatomic potentials, allowing for inexpensive reactive AIMD simulations at quantum mechanical accuracy. We demonstrate the approach by accurately simulating post-TS dynamical effects in three distinct pericyclic reactions, including a challenging trispericyclic reaction with a complex bifurcating potential energy surface. This approach is broadly applicable to understanding dynamical effects and predicting reaction outcomes in large, previously intractable systems.

A Survey on IoT Big Data

Driven by the core technologies, i.e., sensor-based autonomous data acquisition and the cloud-based big data analysis, IoT automates the actuation of data-driven intelligent actions on the connected objects. This automation enables numerous useful real-life use-cases, such as smart transport, smart living, smart cities, and so on. However, recent industry surveys reflect that data-related challenges are responsible for slower growth of IoT in recent years. For this reason, this article presents a systematic and comprehensive survey on IoT Big Data (IoTBD) with the aim to identify the uncharted challenges for IoTBD. This article analyzes the state-of-the-art academic works in IoT and big data management across various domains and proposes a taxonomy for IoTBD management. Then, the survey explores the IoT portfolio of major cloud vendors and provides a classification of vendor services for the integration of IoT and IoTBD on their cloud platforms. After that, the survey identifies the IoTBD challenges in terms of 13 V’s challenges and envisions IoTBD as “Big Data 2.0.” Then the survey provides comprehensive analysis of recent works that address IoTBD challenges by highlighting their strengths and weaknesses to assess the recent trends and future research directions. Finally, the survey concludes with discussion on open research issues for IoTBD.

Evaluation of induced trench twin culverts constructed in deep fill

When culverts are installed beneath high embankments, earth loads become excessive and the induced trench construction method is a viable design option to reduce the culvert loads to acceptable levels. However, limited field studies evaluating the performance of induced trench twin culverts are reported in the literature and the practicality and effectiveness of the induced trench construction method (in general) has been subject to recent criticism. This paper describes the performance of twin 3048 mm inside-diameter reinforced concrete culverts constructed with an induced trench beneath 15.3 m of fill. Research instruments and autonomous data acquisition systems were installed during construction to monitor (i) culvert earth pressures, (ii) embankment deformations, and (iii) groundwater elevations in the vicinity of the compressible fill. The experimental observations recorded throughout the construction phase are presented herein; the embankment deformations are indicative of effective positive arching within the induced trench region, and the average earth pressure at the culvert crown was reduced to approximately 48% of the overburden soil pressure. The experimental data are compared with those reported in the literature by others, and the conclusions attained from this study demonstrate the effectiveness of the induced trench construction method.

A 5-Channel Unipolar Fetal-ECG Readout IC for Patch-Based Fetal Monitoring

This letter presents a 5-channel unipolar fetal electrocardiogram readout IC for monitoring the health of a fetus during pregnancy. Each readout channel includes an instrumentation amplifier, a programable gain amplifier and a successive approximation register ADC. A unipolar, common half branch reuse topology is used to achieve low noise, low power, low crosstalk between the channels high input impedance and high CMRR at the same time. Each channel achieves an input referred noise of 0.47 μVrms in 0.5 to 150 Hz, while consuming a power of 43.2 μW. The 5-channel system provides a CMRR of 98 dB and an interchannel crosstalk rejection of 95 dB. The chip is implemented in a standard 55-nm CMOS process and occupies an area of 4.0 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The whole chip, including five readout channels, leadoff detection, reference generation, autonomous data acquisition with on-chip sample storage and an interrupt-based serial peripheral host interface consumes a total power of 258 μW.

Application and Development of Rehabilitation System of Intelligent Lower Limb Rehabilitation Based on Intelligent Medical Treatment

To develop an intelligent lower limb rehabilitation instrument which could realize the quantification and visualization of lower limbs' raising angle and frequency, using the smart client to realize the remote control, autonomous data acquisition and the establishment of database. Doctors had the access to the database in order to adjust the rehabilitation program in time to meet the individual requirement. We realized the design of intelligent lower limb instrument based on the Andriod smartphone, which is suitable for clinical and family use.

Access Control and Resource Allocation for M2M Communications in Industrial Automation

Machine-to-machine communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet applications such as industrial automation. In this paper, we develop a two-stage access control and resource allocation algorithm. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without prior knowledge of channel states. Particularly, the joint power allocation and channel selection problem is formulated as a two-dimensional matching problem, and solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified under various simulation scenarios.

An Integrated Energy-Efficient Wireless Sensor Node for the Microtremor Survey Method

The microtremor survey method (MSM) has the potential to be an important geophysical method for identifying the strata velocity structure and detecting the buried fault structures. However, the existing microtremor exploration equipment has been unable to satisfy the requirements of the MSM, which suffers from low data accuracy, long measurement time, and blind acquisition. In this study, we combined a 2 Hz moving coil geophone with advanced acquisition systems to develop a new integrated energy-efficient wireless sensor node for microtremor exploration. A high-precision AD chip and noise matching technology are used to develop a low-noise design for the sensor node. Dynamic frequency selection technology (DFS) and dynamic power management technology (DPM) are used to design an energy-efficient mode. The data quality monitoring system solves the closed technical flaws between the acquisition systems and the control center via 4G wireless monitoring technologies. According to the results of a series of in situ tests and field measurements, the noise level of the system was 0.7 μV@500 Hz with 0 dB attenuation and 220 mW power consumption of the system in the autonomous data acquisition mode. Therefore, it provides substantial support for the effective data acquisition over long measurement durations in microtremor exploration processes. The applicability of the system is evaluated using field data, according to which the integrated energy-efficient wireless sensor node is convenient and effective for MSM.

Прецизионная автономная модульная 24-разрядная система сбора геофизических данных

Прецизионная автономная модульная 24-разрядная система сбора геофизических данных

Inexpensive Multimodal Sensor Fusion System for Autonomous Data Acquisition of Road Surface Conditions

This paper presents the development, evaluation, calibration, and field application of a novel, relatively inexpensive, vision-based sensor system employing commercially available off-the-shelf devices, for enabling the autonomous data acquisition of road surface conditions. Detailed evaluations and enhancements of a variety of technical approaches and algorithms for overcoming vision-based measurement distortions induced by the motion of the monitoring platform were conducted. It is shown that the proposed multi-sensor system, by capitalizing on powerful data-fusion approaches of the type developed in this paper, can provide a robust cost-effective road surface monitoring system with sufficient accuracy to satisfy typical maintenance needs, in regard to the detection, localization, and quantification of potholes and similar qualitative deterioration features where the measurements are acquired via a vehicle moving at normal speeds on typical city streets. The proposed system is ideal to be used for crowdsourcing where several vehicles would be equipped with this cost-effective system for more frequent data collection of road surfaces. Suggestions for future research needs to enhance the capabilities of the proposed system are included.