Abstract: Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault diagnosis has some deficiencies, such as complex computation of feature vectors, excessive dependence on prior knowledge and diagnostic expertise, and limited capacity for learning complex relationships in fault signals. Furthermore, following the increase in condition data, how to promptly process the massive fault data and automatically provide accurate diagnosis has become an urgent need to solve. Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines. In this paper, a nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain. For exploring the discrimination hidden in the acquired data, a stacked sparse autoencoders-based deep neural network is established and performed with an unsupervised learning procedure followed by a supervised fine-tuning process. We studied the significance of compressed acquisition and provided the effects of key factors and comparison with traditional methods. The effectiveness of the proposed method is validated using data sets from rolling element bearings and the analysis shows that it is able to obtain high diagnotic accuracies and is superior to the existing methods. The proposed method reduces the need of human labor and expertise and provides new strategy to handle the massive data more easily.

Abstract: Acquisition, analysis and simulation of electrophysiological properties of the nervous system require multiple software packages. This makes it difficult to conserve experimental metadata and track the analysis performed. It also complicates certain experimental approaches such as online analysis. To address this, we developed NeuroMatic, an open-source software toolkit that performs data acquisition (episodic, continuous and triggered recordings), data analysis (spike rasters, spontaneous event detection, curve fitting, stationarity) and simulations (stochastic synaptic transmission, synaptic short-term plasticity, integrate-and-fire and Hodgkin-Huxley-like single-compartment models). The merging of a wide range of tools into a single package facilitates a more integrated style of research, from the development of online analysis functions during data acquisition, to the simulation of synaptic conductance trains during dynamic-clamp experiments. Moreover, NeuroMatic has the advantage of working within Igor Pro, a platform-independent environment that includes an extensive library of built-in functions, a history window for reviewing the user's workflow and the ability to produce publication-quality graphics. Since its original release, NeuroMatic has been used in a wide range of scientific studies and its user base has grown considerably. NeuroMatic version 3.0 can be found at http://www.neuromatic.thinkrandom.com and https://github.com/SilverLabUCL/NeuroMatic.

Abstract: Single-crystal electron diffraction (SCED) is emerging as an effective technique to determine and refine the structures of unknown nano-sized crystals. In this work, the implementation of the continuous rotation electron diffraction (cRED) method for high-throughput data collection is described. This is achieved through dedicated software that controls the transmission electron microscope and the camera. Crystal tracking can be performed by defocusing every nth diffraction pattern while the crystal rotates, which addresses the problem of the crystal moving out of view of the selected area aperture during rotation. This has greatly increased the number of successful experiments with larger rotation ranges and turned cRED data collection into a high-throughput method. The experimental parameters are logged, and input files for data processing software are written automatically. This reduces the risk of human error, and makes data collection more reproducible and accessible for novice and irregular users. In addition, it is demonstrated how data from the recently developed serial electron diffraction technique can be used to supplement the cRED data collection by automatic screening for suitable crystals using a deep convolutional neural network that can identify promising crystals through the corresponding diffraction data. The screening routine and cRED data collection are demonstrated using a sample of the zeolite mordenite, and the quality of the cRED data is assessed on the basis of the refined crystal structure.

Abstract: Principal component analysis (PCA) is a fundamental primitive of many data analysis, array processing, and machine learning methods. In applications where extremely large arrays of data are involved, particularly in distributed data acquisition systems, distributed PCA algorithms can harness local communications and network connectivity to overcome the need of communicating and accessing the entire array locally. A key feature of distributed PCA algorithm is that they defy the conventional notion that the first step toward computing the principal vectors is to form a sample covariance. This paper is a survey of the methodologies to perform distributed PCA on different data sets, their performance, and of their applications in the context of distributed data acquisition systems.

Abstract: A monitoring apparatus, systems and methods for data collection in an industrial environment are disclosed. A system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, wherein the data collector collects data based on a selected data collection routine, a data storage structured to store a plurality of collector routes and collected data, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data and determine an aggregate rate of data being collected from the plurality of input channels, wherein if the aggregate rate exceeds a throughput parameter of the network infrastructure, then the data analysis circuit alters the data collection to reduce the amount of data collected.

Abstract: Internet of Multimedia Things (IoMT) faces the challenge of how to realize low-cost data acquisition while still preserve data confidentiality. In this paper, we present a low-cost and confidentiality-preserving data acquisition framework for IoMT. First, we harness chaotic convolution and random subsampling to capture multiple image signals. The measurement matrix is under the control of chaos, ensuring the security of the sampling process. Next, we assemble these sampled images into a big master image, and then encrypt this master image based on Arnold transform and single value diffusion. The computation of these two transforms only requires some low-complexity operations. Finally, the encrypted image is delivered to cloud servers for storage and decryption service. Experimental results demonstrate the security and effectiveness of the proposed framework.

Abstract: In the past few years the utilization of biological signals as a method of interface with a robotic device has become increasingly more prominent. With the many of these systems being based on EEG and EMG.EMG based control has five main parts data acquisition, signal conditioning, feature extraction, classification, and control. This paper seeks to briefly cover the aspects of data acquisition and signal conditioning. After which, various methods of feature extraction, and classification are discussed.

Abstract: The wide-area measurement system provides a new data acquisition and supervisory control tool for a power system, and the data acquisition level is increased dramatically with its development in the smart grid environment Huge data associated with the power system operation are acquired, which are beneficial for enhancing situational awareness of a power system concerned Identifying the coherency among synchronous generators using real-time signals from phasor measurement units (PMUs) is one of the major tasks of situational awareness in power system operation Given this background, a data-driven coherency identification methodology is proposed based on the spectral clustering algorithm First, several trajectory dissimilarity indices for the rotor angle and rotor speed trajectories of generators as measured by PMUs are presented based on the trajectory similarity theory Second, a decision-making method based on the Gini coefficient and Kendall rank correlation coefficient is presented for integrating multiple indices describing trajectory dissimilarities Third, the spectral clustering algorithm is presented to identify the coherency of synchronous generators, and silhouette is presented for determining a reasonable number of coherent groups Finally, oscillation events happened/simulated in two actual power systems, ie, Guangdong power system in China and Western Interconnection power system in North America, are utilized to demonstrate the effectiveness of the proposed data-driven coherency identification methodology

Abstract: In electrical impedance tomography (EIT), it is difficult to obtain the intracranial impedance due to the highly resistive skull enclosing the brain. Therefore, a high-precision data acquisition system is required for brain EIT. In this paper, we used a high-precision digital synthesis method and a digital demodulation technique with high noise immunity to eliminate random errors. Moreover, we focused on two problems encountered during EIT data acquisition: 1) the shunt effect on the excitation current due to the distributed capacitance between electrodes and ground and 2) high common-mode voltages in the boundary measurements. We designed a new electrode interface to reduce the influence of the distributed capacitance and a programmable current source to accurately compensate for the excited current. We also proposed a new voltmeter circuit with improved CMRR. Overall, this EIT data acquisition system can produce a programmable current with SNR greater than 89 dB. It can also measure the voltage difference precisely with CMRR higher than 75 dB with a 1- $\text{k}\Omega $ impedance imbalance. The results on a calibration model show that this system has a high SNR of 83 dB and a low reciprocity error of 0.125%. In addition, EIT imaging results were acquired using a brain physical phantom. The system can detect small disturbances of 0.35% in volume (1.99% of the cross-sectional area) and 17% in resistivity. Experiments on healthy volunteers also suggest that small intracranial impedance variations due to temporary occlusion and reperfusion of the unilateral carotid artery may be monitored by the system.

Abstract: Multibeam echosounders are becoming widespread for the purposes of seafloor bathymetry mapping, but the acquisition and the use of seafloor backscatter measurements, acquired simultaneously with the bathymetric data, are still insufficiently understood, controlled and standardized. This presents an obstacle to well-accepted, standardized analysis and application by end users. The Marine Geological and Biological Habitat Mapping group (Geohab.org) has long recognized the need for better coherence and common agreement on acquisition, processing and interpretation of seafloor backscatter data, and established the Backscatter Working Group (BSWG) in May 2013. This paper presents an overview of this initiative, the mandate, structure and program of the working group, and a synopsis of the BSWG Guidelines and Recommendations to date. The paper includes (1) an overview of the current status in sensors and techniques available in seafloor backscatter data from multibeam sonars; (2) the presentation of the BSWG structure and results; (3) recommendations to operators, end-users, sonar manufacturers, and software developers using sonar backscatter for seafloor-mapping applications, for best practice methods and approaches for data acquisition and processing; and (4) a discussion on the development needs for future systems and data processing. We propose for the first time a nomenclature of backscatter processing levels that affords a means to accurately and efficiently describe the data processing status, and to facilitate comparisons of final products from various origins.

Abstract: A monitoring system for data collection in an industrial drilling environment comprising a data collector communicatively coupled to a plurality of input channels and to network infrastructure, wherein the data collector is sensitive to a change to a parameter of the network infrastructure within the environment; a data storage structured to store a plurality of collector routes, each comprising a different data collection routine, and collected data that corresponds to the input channels; a data acquisition circuit structured to interpret a plurality of detection values from the collected data corresponding to one of the input channels; and a data analysis circuit structured to analyze the collected data from the plurality of input channels and evaluate a selected collection routine of the data collector, wherein the selected collection routine is switched to a second collection routine due to the data analysis circuit detecting a change to a network infrastructure parameter.

Abstract: X-rays offer high penetration with the potential for tomography of centimetre-sized specimens, but synchrotron beamlines often provide illumination that is only millimetres wide. Here an approach is demonstrated termed Tomosaic for tomographic imaging of large samples that extend beyond the illumination field of view of an X-ray imaging system. This includes software modules for image stitching and calibration, while making use of existing modules available in other packages for alignment and reconstruction. The approach is compatible with conventional beamline hardware, while providing a dose-efficient method of data acquisition. By using parallelization on a distributed computing system, it provides a solution for handling teravoxel-sized or larger datasets that cannot be processed on a single workstation in a reasonable time. Using experimental data, the package is shown to provide good quality three-dimensional reconstruction for centimetre-sized samples with sub-micrometre pixel size.

Abstract: The unmanned research aircraft ALADINA (Application of Light-weight Aircraft for Detecting in situ Aerosols) has been established as an important tool for boundary layer research For simplified integration of additional sensor payload, a flexible and reliable data acquisition system was developed at the Institute of Flight Guidance, Technische Universitat (TU) Braunschweig The instrumentation consists of sensors for temperature, humidity, three-dimensional wind vector, position, black carbon, irradiance and atmospheric particles in the diameter range of ultra-fine particles up to the accumulation mode The modular concept allows for straightforward integration and exchange of sensors So far, more than 200 measurement flights have been performed with the robustly-engineered system ALADINA at different locations The obtained datasets are unique in the field of atmospheric boundary layer research In this study, a new data processing method for deriving parameters with fast resolution and to provide reliable accuracies is presented Based on tests in the field and in the laboratory, the limitations and verifiability of integrated sensors are discussed

Abstract: Ultrahigh speed optical coherence tomography (OCT) systems with >100 kHz A-scan rates can generate volumes rapidly with minimal motion artifacts and are well suited for 4D imaging (volumes through time) applications such as intra-operative imaging. In such systems, high OCT data acquisition efficiency (defined as the fraction of usable A-scans generated during the total acquisition time) is desired to maximize the volumetric frame rate and sampling pitch. However, current methods for beam scanning using non-resonant and resonant mirror scanners can result in severe scan distortion and transverse oversampling as well as require acquisition dead times, which limit the acquisition efficiency and performance of ultrahigh speed 4D OCT. We introduce constant linear velocity spiral scanning (CLV-SC) as a novel beam scanning method to maximize the data acquisition efficiency of ultrahigh speed 4D OCT systems. We demonstrate that CLV-SC does not require acquisition dead times and achieves more uniform transverse sampling compared to raster scanning. To assess its clinical utility, we implement CLV-SC with a 400 kHz OCT system and image the anterior eye and retina of healthy adults at up to 10 volumes per second with isotropic transverse sampling, allowing B-scans with equal sampling pitch to be extracted from arbitrary locations within a single volume. The feasibility of CLV-SC for intra-operative imaging is also demonstrated using a 800 kHz OCT system to image simulated retinal surgery at 15 volumes per second with isotropic transverse sampling, resulting in high quality volume renders that enable clear visualization of surgical instruments and manipulation of tissue.

Abstract: This project addresses the need for the implementation of low-cost acquisition technology in the field of vehicle engineering: the design, development, manufacture, and verification of a low-cost Arduino-based data acquisition platform to be used in <80 Hz data acquisition in vehicle dynamics, using low-cost accelerometers. In addition to this, a comparative study is carried out of professional vibration acquisition technologies and low-cost systems, obtaining optimum results for low- and medium-frequency operations with an error of 2.19% on road tests. It is therefore concluded that these technologies are applicable to the automobile industry, thereby allowing the project costs to be reduced and thus facilitating access to this kind of research that requires limited resources.

Abstract: Data acquisition software is an essential component of modern macromolecular crystallography (MX) beamlines, enabling efficient use of beam time at synchrotron facilities. Developed at the Paul Scherrer Institute, the DA+ data acquisition software is implemented at all three Swiss Light Source (SLS) MX beamlines. DA+ consists of distributed services and components written in Python and Java, which communicate via messaging and streaming technologies. The major components of DA+ are the user interface, acquisition engine, online processing and database. Immediate data quality feedback is achieved with distributed automatic data analysis routines. The software architecture enables exploration of the full potential of the latest instrumentation at the SLS MX beamlines, such as the SmarGon goniometer and the EIGER X 16M detector, and development of new data collection methods.

Abstract: The aim of this paper is to propose an IoT-based model for real-time condition monitoring of electrical machines, which addresses the challenges of data storage and scalability. The proposed model is evolved with an experimental setup having two sets of dc motor coupled to ac generator and an IoT device to elucidate integrated monitoring and decision making. This IoT-based vibration analytic model uses an IoT2040 Gateway with custom Linux OS image built for acquisition and streaming of vibration signals. The Python target application acquires dc motors shaft vibration using vibration sensors and communicates the data as events to cloud through serial device driver interface. The IoT service running in cloud receives the data from multiple machines through lightweight RESTful HTTP and records the same which are retrievable for analysis and algorithm development in any platform. The retrieved data have been analyzed using the proposed statistical classification-based signal decomposition algorithm as well as time-frequency analysis to estimate the vibration thresholds of every machine connected to IoT cloud. Such estimated thresholds corresponding to different operating and environmental conditions maintained in cloud are used to build a repository of context specific solutions for machine conditions leading to improved maintenance decisions. The uniformity of threshold values obtained from IoT-based model in comparison with that of analysis carried out on the machines locally using myRIO for data acquisition ensures the integrity of the proposed statistical classification algorithm and reliability of the IoT model for condition monitoring with assured scalability.

Abstract: Due to specific dynamics of the operating environment and required safety regulations, the amount of acquired data of an automotive LiDAR sensor that has to be processed is reaching several Gbit/s. Therefore, data compression is much-needed to enable future multi-sensor automated vehicles. Numerous techniques have been developed to compress LiDAR raw data; however, these techniques are primarily targeting a compression of 3D point cloud, while the way data is captured and transferred from a sensor to an electronic computing unit (ECU) was left out. The purpose of this paper is to discuss and evaluate how various low-level compression algorithms could be used in the automotive LiDAR sensor in order to optimize on-chip storage capacity and link bandwidth. We also discuss relevant parameters that affect amount of collected data per second and what are the associated issues. After analyzing compressing approaches and identifying their limitations, we conclude several promising directions for future research.

Abstract: In this paper, we present the design, development, and validation of a ‘modular photoplethysmography (PPG) system called ZenPPG. This portable, dual-channel system has the capability to produce “raw” PPG signals at two different wavelengths using commercial and/or custom-made PPG sensors. The system consists of five modules, each consisting of circuitry required to perform specific tasks, and are all interconnected by a system bus. The ZenPPG system also facilitates the acquisition of other physiological signals on-demand including electrocardiogram (ECG), respiration, and temperature signals. This report describes the technical details and the evaluation of the ZenPPG along with results from a pilot in vivo study on healthy volunteers. The results from the technical evaluations demonstrate the superiority and flexibility of the system. Also, the systems’ compatibility with commercial pulse oximetry sensors such as the Masimo reusable sensors was demonstrated, where good quality raw PPG signals were recorded with the signal-to-noise ratio (SNR) of 50.65 dB. The estimated arterial oxygen saturation (SpO2) values from the system were also in close agreement with commercial pulse oximeters, although the accuracy of the reported SpO2 value is dependent on the calibration function used. Future work is targeted toward the development of variations of each module, including the laser driver and fiber optic module, onboard data acquisition and signal processing modules. The availability of this system will help researchers from a wide range of disciplines to customize and integrate the ZenPPG system to their research needs and will most definitely enhance research in related fields.

Abstract: This paper presents implementation report for slope monitoring on Wireless Sensor Network (WSN) system by use of IEEE802.15.4 (ZigBee). The purpose of this research is the construction of WSN system which can be sensing and monitoring the deformation of the ground caused by heavy rain in mountain areas with radio communication devices and sensor devices. For the stable data acquisition, some electrical and environmental problems should be solved. In this paper, the techniques for dynamical transition of the communication mode depending on battery capacity, a method of protecting wireless nodes from the lighting, and a optimized design of antenna for WSN are proposed. The experimental results which the proposed system installed at actual test field show that the dynamic wireless network configuration. Moreover, the status of field can be predicted by the obtained data from the improved system.

Abstract: We present a challenging and realistic novel dataset for evaluating 6-DOF object tracking algorithms. Existing datasets show serious limitations—notably, unrealistic synthetic data, or real data with large fiducial markers—preventing the community from obtaining an accurate picture of the state-of-the-art. Using a data acquisition pipeline based on a commercial motion capture system for acquiring accurate ground truth poses of real objects with respect to a Kinect V2 camera, we build a dataset which contains a total of 297 calibrated sequences. They are acquired in three different scenarios to evaluate the performance of trackers: stability, robustness to occlusion and accuracy during challenging interactions between a person and the object. We conduct an extensive study of a deep 6-DOF tracking architecture and determine a set of optimal parameters. We enhance the architecture and the training methodology to train a 6-DOF tracker that can robustly generalize to objects never seen during training, and demonstrate favorable performance compared to previous approaches trained specifically on the objects to track.

Abstract: Measurements using the transient electromagnetic (TEM) method are unavoidably contaminated by noise from various sources. Knowledge of the local noise conditions during data acquisition can be useful for tuning the acquisition parameters in the field and for quantification of uncertainties in the interpretation phase. Normally, assessment of the noise conditions requires dedicated noise measurements with the TEM transmitter off. We have developed a frequency-domain method for estimating noise models, in the form of noise power spectra, from data acquired with the TEM system in normal operation with the transmitter on. The method is based on a well-known method for power spectral density estimation, combined with a windowing approach specifically tailored for the characteristics of TEM signals and interpolation in the frequency domain. Because this method does not require the survey to be interrupted, the noise model can be updated continuously, with no added survey cost or inconvenience, to reveal...

Abstract: Phased array methods are a promising approach to damage detection by enabling guided wave steering and beam focusing leading to improved localization and sizing of structural damage. Due to the costs and challenges to addressing piezoelectric arrays in parallel, most phased array methods actuate piezoelectric elements one at a time in round‐robin fashion with postprocessing algorithms used to synthetically steer and focus the guided waves. In this study, true parallel excitation and sensing of ceramic piezoelectric actuators is implemented in a standard structural health monitoring data acquisition system without requiring highly specialized and expensive ultrasonic data acquisition equipment. The study performs both numerical simulation and laboratory experiments to illustrate the damage detection capabilities of a piezoelectric phased array. Laboratory experiments are first performed using the high‐speed data acquisition system to actuate and sense piezoelectric elements bonded in a linear array on an aluminum plate. To visualize the direction and focal point of the guided waves, a laser Doppler vibrometer triggered by the data acquisition system is adopted to scan the surface displacements of the plate. The study verifies that the location and size of damage can be accurately detected in the plate using the phased array operated by the structural health monitoring data acquisition system. To explore the capabilities of the system to detect different damage cases, the computationally efficient local interaction simulation approach is implemented on a graphic processing unit to simulate the performance of a linear phased array to steer and focus Lamb waves for damage detection.

Abstract: Measuring radiation dosage rates is becoming more and more important in many applications scenarios. Continuously monitoring radiation in contaminated and poorly accessible areas is challenging due to the frequent data collection/transmission combined with long life requirements. We present a self-sustainable wireless sensor node for low power, high precision radiation dosage rate monitoring. We propose an energy-efficient data acquisition algorithm that can reduce the energy per measurement, while guaranteeing minimal loss of precision. The proposed node is designed to work in collaboration with an unmanned aerial vehicle used for two essential mission steps: air-deployment of the wireless sensor nodes at suitable locations, and acquiring data logs via low-power, short-range radio communication in fly-by mode after a wake-up command. The system uses off-the-shelf components for defining the mission, drop-zone, and trajectory, for compressing data and managing communication. The node is equipped with a novel low-power nuclear radiation sensor, and has been designed and implemented with self-sustainability in mind as it will be deployed in hazardous, inaccessible areas. To this end, the proposed node uses a combination of complementary techniques: a low-power microcontroller with non-volatile memory, energy harvesting, adaptive power management, and a nano-watt wake-up radio. Experimental results demonstrate the precision and the low energy consumption of the radiation sensor, the energy efficiency of the whole solution, and the acquisition algorithms. The node consumes only 31 $\mu \text{W}$ in sleep mode and 1.7mW in active mode, and has the capability to achieve perpetual monitoring once deployed.

Abstract: In order to speed up the information construction process of data collection and analysis for smart campus, this paper aiming at the problems existing in the smart campus planning and current research. The acquisition network of underlying data is designed using 6LoWPAN wireless sensor technology which based on IPv6 technology and has the advantages of low power consumption, short distance and low requirement of hardware resources. At the same time, the embedded gateway is designed to interconnection of 6LoWPAN network and Ethernet based on 6–4 tunnel technology and the Netfilter framework. In addition, the sharing platform of data resource is designed using the C/S architecture, and realization the data acquisition system for smart campus based on 6LoWPAN wireless sensor technology. Again, the acquisition of variety data and equipment management is achieved by designing of data acquisition rules, data acquisition drive, data acquisition equipment online management. Finally, the problem of data storage and management of the smart campus data acquisition system is solved by using the powerful storage and processing capabilities for date of Hadoop distributed system. At the same time, the safe, accurate service to date end is realized by data models encapsulate services. Therefore, smart campus is using the new generation of information technology such as internet of things, cloud computing and big data to perceive, store, manage and analyse all the key information of campus system, and serves the teachers and students in the campus.

Abstract: Measurement issues leading to the acquisition of artifact-free shock wave pressure-time profiles are discussed. We address the importance of in-house sensor calibration and data acquisition sampling rate. Sensor calibration takes into account possible differences between calibration methodology in a manufacturing facility, and those used in the specific laboratory. We found in-house calibration factors of brand new sensors differ by less than 10% from their manufacturer supplied data. Larger differences were noticeable for sensors that have been used for hundreds of experiments and were as high as 30% for sensors close to the end of their useful lifetime. These observations were despite the fact that typical overpressures in our experiments do not exceed 50 psi for sensors that are rated at 1000 psi maximum pressure. We demonstrate that sampling rate of 1000 kHz is necessary to capture the correct rise time values, but there were no statistically significant differences between peak overpressure and impulse values for low-intensity shock waves (Mach number < 2) at lower rates. We discuss two sources of experimental errors originating from mechanical vibration and electromagnetic interference on the quality of a waveform recorded using state-of-the-art high-frequency pressure sensors. The implementation of preventive measures, pressure acquisition artifacts and data interpretation with examples, are provided in this paper that will help the community at large to avoid these mistakes. In order to facilitate inter-laboratory data comparison, common reporting standards should be developed by the blast TBI research community. We noticed the majority of published literature on the subject limits reporting to peak overpressure; with much less attention directed towards other important parameters, i.e., duration, impulse and dynamic pressure. These parameters should be included as a mandatory requirement in publications so the results can be properly compared with others.