Abstract: Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Recovering images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.

Abstract: Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare dama...

Abstract: Underwater exploration activities have grown significantly due to the proliferation of underwater Internet of Things (UIoT). However, to transmit sensor data from UIoT to remote onshore data processing center requires a huge cost of deploying and maintaining communication infrastructures. In this article, we propose an unmanned aerial vehicles (UAVs)-assisted underwater data acquisition scheme by placing multiple sink nodes on the water surface to serve as intermediate relays between underwater sensors (IoT nodes) and UAVs. In our scheme, the sensor data are first transmitted via an acoustic-signal link to a buoyant sink node, which then forwards the data to a UAV via an electromagnetic link. In particular, we adopt two sink-node-deployment methods, i.e., grid placement and random placement of sink nodes. Since the path connectivity from an underwater sensor node to the UAV is crucial to guarantee reliable data acquisition tasks, we establish a theoretical framework to analyze the path connectivity via the intermediate sink node for both grid and random sink-node-deployment methods. Extensive simulation results validate the accuracy of the proposed analytical model. Moreover, our results also reveal the relationship between the path connectivity and other factors, such as sink node placements, antenna beamwidth of UAVs, and wind speed. We also further extend our UAV-assisted data acquisition to other scenarios with the consideration of trajectories of UAVs, movements of sink nodes, interference of both underwater acoustic and terrestrial radio links, and integration with edge computing.

Abstract: The present study applies the one-dimensional convolutional neural network (1D-CNN) to propose an intelligent approach of the feature extraction for the analog circuit diagnosis. The raw signals based on various soft faults from the output terminal of the circuit under test (CUT) are collected with appropriate data acquisition system to implement a data-driven fault diagnosis. The data-driven diagnosis process is typically encapsulated in two distinct blocks, including the feature extraction and the classification. In this study, the designed 1D-CNN model efficiently combines the aforementioned two phases into a single diagnosis body with fast learning rate and accurate classification. The main advantages of the 1D-CNN are: 1) it can be directly established to the raw signal with proper training so that it is more applicable in real applications; 2) its compact architecture and configuration has reasonable applicability in complex analog circuits; 3) convolutional kernels guarantee that the hierarchical features can be extracted from raw data with better anti-interference performance. Moreover, since the method can extract high-level features of raw signals, it resolves the necessity to employ other per-processing methods for the hand-crafted feature transformation. The performance of the proposed 1D-CNN model is evaluated through three benchmark circuits on the SIMULINK platform. Obtained results are compared with other intelligent fault diagnosis methods. The experimental results show that the 1D-CNN can be utilized effectively as the feature exactor and faults classifier for analog circuits.

Abstract: Simultaneous source technology can accelerate data acquisition and improve subsurface illumination. But those advantages are compromised due to dense interference. To address the intense in...

Abstract: In this article, we present a novel digital predistortion (DPD) architecture for multiple-input–multiple-output (MIMO) transmitters using a real-time single-channel over-the-air (OTA) data acquisition loop. The proposed feedback data acquisition strategy captures OTA signals from a fixed location and indirectly identifies the nonlinear behavior of all power amplifiers (PAs) in the array, as well as their combined signals in the far-field direction. The DPD can, therefore, be effectively constructed without direct measurement at PA output or at user end. The proposed linearization solution can run in real-time and, thus, does not interfere with data transmission in the MIMO transmitters. It can also achieve robust performance when mutual coupling occurs between antenna elements. Simulation and experimental results demonstrate that the proposed scheme can accurately estimate both PA outputs and far-field main beam data. Excellent linearization performance can be achieved with low complexity hardware implementation and reduced computational complexity.

Abstract: This work proposes a generic method to utilize customer smart meter measurements to automatically and simultaneously estimate topology, line parameters, and customer and line phasing connections in low voltage (LV) distribution systems. This generic approach is applicable to single, two, and three-phases lines and customers. Hence, it is suitable not only for North American systems but also for European and South American systems. This generic estimation is conducted by using a multiple linear regression model applied to data supplied by customers meters. The acceptance of each estimated parameter is carried out through comparisons with mathematical (e.g., coefficient of determination and relative standard deviation) and physical constraints (e.g., resistances, line length, and conductor X/R ratios). Granularity and sensitivity analyses are also conducted taking into account smart meter data quality (e.g., update ratio, metering errors, resolution, clock desynchronization). The obtained results show the high performance of the method to correctly estimate the system topology, line parameters, and line and customers phasing based on a specification of 15-day sample size with 60-min resolution as a general compromise solution between data acquisition and accuracy.

Abstract: Cryo electron tomography with subsequent subtomogram averaging is a powerful technique to structurally analyze macromolecular complexes in their native context. Although close to atomic resolution in principle can be obtained, it is not clear how individual experimental parameters contribute to the attainable resolution. Here, we have used immature HIV-1 lattice as a benchmarking sample to optimize the attainable resolution for subtomogram averaging. We systematically tested various experimental parameters such as the order of projections, different angular increments and the use of the Volta phase plate. We find that although any of the prominently used acquisition schemes is sufficient to obtain subnanometer resolution, dose-symmetric acquisition provides considerably better outcome. We discuss our findings in order to provide guidance for data acquisition. Our data is publicly available and might be used to further develop processing routines. Here the authors systematically benchmark cryo-electron tomography acquisition schemes to optimize the attainable resolution for subtomogram averaging, and find that dose-symmetric acquisition with even angular sampling provides a better outcome than most currently used acquisition schemes.

Abstract: Automatic monitoring system is one of the main means to ensure the safety of underground engineering construction This paper summarizes the current international research and application status of the underground engineering monitoring system from three aspects of data acquisition, data transmission, and data processing and emphatically introduces the mainstream new technology of the monitoring system Furthermore, this paper puts forward specific and implementable technical routes based on the current intelligent technology and the challenges faced by future monitoring, which can provide direction and reference for future research, including high-precision real-time acquisition and safe and reliable transmission of monitoring data, multisource data fusion, and the visual intelligent early warning platform

Abstract: The objective of the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is to evaluate a set of lab-based behavioral video tracking (VT), electroencephalography (EEG), and eye tracking (ET) measures for use in clinical trials with children with autism spectrum disorder (ASD). Within the larger organizational structure of the ABC-CT, the Data Acquisition and Analytic Core (DAAC) oversees the standardization of VT, EEG, and ET data acquisition, data processing, and data analysis. This includes designing and documenting data acquisition and analytic protocols and manuals; facilitating site training in acquisition; data acquisition quality control (QC); derivation and validation of dependent variables (DVs); and analytic deliverables including preparation of data for submission to the National Database for Autism Research (NDAR). To oversee consistent application of scientific standards and methodological rigor for data acquisition, processing, and analytics, we developed standard operating procedures that reflect the logistical needs of multi-site research, and the need for well-articulated, transparent processes that can be implemented in future clinical trials. This report details the methodology of the ABC-CT related to acquisition and QC in our Feasibility and Main Study phases. Based on our acquisition metrics from a preplanned interim analysis, we report high levels of acquisition success utilizing VT, EEG, and ET experiments in a relatively large sample of children with ASD and typical development (TD), with data acquired across multiple sites and use of a manualized training and acquisition protocol.

Abstract: With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed CNU-TS-2 mobile tunnel monitoring system for data acquisition, which has an electric system to control its forward speed and is compatible with various laser scanners such as the Faro and Leica models. A comparison with corresponding data acquired by total station data demonstrates that the data collected by CNU-TS-2 is accurate. Following data acquisition, the overall and local deformation of the tunnel is determined by denoising and 360° deformation analysis of the point cloud data. To enhance the expression of the analysis results, this study proposes an expansion of the tunnel point cloud data into a two-dimensional image via cylindrical projection, followed by an expression of the tunnel deformation through color difference to visualize the deformation. Compared with the three-dimensional modeling method of visualization, this method is easier to implement and facilitates storage. In addition, it is conducive to the performance of comprehensive analysis of problems such as water leakage in the tunnel, thereby achieving the effect of multiple uses for a single image.

Abstract: Named Data Networking (NDN) might potentially help improve data acquisition efficiency in vehicular environments, so it is introduced into vehicular networks, namely vehicular NDN (VNDN). In the existing VNDN solutions, a large number of vehicles with high mobility are involved in forwarding messages, so it is hard to achieve request aggregation that is the main advantage of NDN and can help reduce the data acquisition delay. Moreover, the solutions employ flooding to achieve data retrieval without consumer mobility support, which increases the data acquisition cost and reduces the data acquisition success rate. In this paper, we propose a novel VNDN framework and aim to reduce the data acquisition delay and cost and improve the data acquisition success rate. In this framework, the cluster-chain vehicular backbone is constructed to enhance the stability of the backbone topology. Based on cluster chains, consumers can employ request aggregation and unicast to acquire data from the nearest provider. Moreover, consumer mobility is supported to guarantee successful retrieval of data in spite of high mobility of vehicles. This framework is quantitatively evaluated. According to the experimental results, compared with the VNDN approaches, our framework reduces the data acquisition cost and delay by nearly 54.6% and 12.3%, respectively, and improves the data retrieval success rate by nearly 7.9%.

Abstract: The installation of smart meters is fast growing to effectively support various smart grid stack holders. Collection and processing of fine-grained metering data is important for proper analysis and decision support. The traditional smart meters are based on standardized and time-invariant tactics to acquire and process the data. This results in the collection, storage, and processing of a huge amount of unneeded data. The focus of this paper is to enhance the contemporary smart meters data acquisition and processing chains. The objective is to attain real-time compression and computational effectiveness to enhance the system performance in terms of data analysis, storage and transmission and to diminish its consumption overhead. In this framework, the signal-piloted event-driven sampling and processing tactics are exploited. The novel adaptive rate techniques are used for data segmentation and extraction of features. Household appliances consumption patterns related features are being classified subsequently. It is realized by employing the mature K-Nearest Neighbor and the Artificial Neural Network classifiers. Results demonstrate a 3.8-fold compression gain and computational effectiveness of the designed solution over traditional counterpart while securing the best classification accuracy of 94.4% for the 6-class appliances dataset.

Abstract: Advancement in sensing devices such as wireless sensors and high-rate data acquisition systems have recently enhanced inherent ability of structural health monitoring (SHM) where a large am...

Abstract: This work consists in the development of a system for data acquisition of parameters in an agricultural application. For this, the system includes the monitoring of soil moisture and atmospheric sensors (temperature and relative humidity), in order to provide subsidies to farmers in decision-making, aiming at a future implementation of an automated irrigation system, with minimization of waste of water resources. Data acquisition is carried out by means of sensors connected to a microcontrolled system, and the signals are transmitted through a radio frequency module using LoRaWan™ protocol. Data is received at a gateway and made available in the cloud, applying Internet of Things (IoT) concepts, and can be monitored in real time in an academic interface. Additionally, the data can also be monitored through a simplified interface accessible through an app developed specifically for the application.

Abstract: This paper aims to reduce the power consumption of electrocardiography based wearable healthcare devices, by introducing power reduction approaches and considerations at system level design, where we have the highest potential to influence power. It focuses, in particular, on algorithm design and implementation, data acquisition, and transmission under constrained resources. A thorough investigation of the suitability of nine existing algorithms for on-sensor QRS feature detection is conducted, with respect to metrics such as sensitivity, positive predictivity, power consumption, parameter choice and time delay. Optimisation of data acquisition on CPU-based IoT systems is performed, and the current consumption is reduced by a factor of 3 using a combination of direct memory access (DMA) list approach and low-level register manipulations for task delegation. The acquisition data rate, sampling rate, buffer and batch size are also optimised. To reduce the power consumption by data transmission, the effect of on-sensor versus off-sensor processing is investigated. While focusing on CPU-based systems with experiments performed on a generic low-power wearable platform, the design optimisation and considerations proposed in this work could be extended to custom designs and allow further investigation into QRS detection algorithm optimisation for wearable devices.

Abstract: The use of fast pixelated detectors and direct electron detection technology is revolutionizing many aspects of scanning transmission electron microscopy (STEM). The widespread adoption of these new technologies is impeded by the technical challenges associated with them. These include issues related to hardware control, and the acquisition, real-time processing and visualization, and storage of data from such detectors. We discuss these problems and present software solutions for them, with a view to making the benefits of new detectors in the context of STEM more accessible. Throughout, we provide examples of the application of the technologies presented, using data from a Medipix3 direct electron detector. Most of our software are available under an open source licence, permitting transparency of the implemented algorithms, and allowing the community to freely use and further improve upon them.

Abstract: The pursuit of low noise in optical instruments for areal surface topography measurement is relevant to many surface types, ranging from super-polished optical surfaces to weakly reflecting or scattering textures that require enhanced signal sensitivity We clarify the definition and experimental methods for quantifying random noise in areal surface topography measurements We also propose a parameter, the topographical noise density, that concisely summarizes the effects of measurement bandwidth To illustrate these ideas, we present results from a commercial phase-shifting interference microscope showing an RMS measurement noise of 003 nm for a 1-s data acquisition of 1 million surface topography image points, after application of a 3 × 3-pixel convolution filter The results follow the expected inverse square root dependence on the data acquisition time for fast averaging of topography maps, resulting in a measurement noise of <001 nm for a 10-s data acquisition

Abstract: In this work, a new frequency shifting technique is proposed. Using this technique, direction (Approaching/Receding) of the target can be determined using a continuous wave (CW) Radar system. In general, in the classical technique, a huge complex computation is required to find the direction of the target using CWR radar which motivated us to develop a simple and straight-forward technique which is the main contribution presented in this paper. For this, a CW Radar system is designed, with required RF subsystems and signal processing unit. For designing the RF section off the shelf RF components are used. An eight-channel analog to digital converter (ADC) and Field Programmable Gate Array (FPGA) is used for analog to digital conversion and data acquisition. To get the target information from the received signal, signal processing is done in MATLAB. To get Doppler information from the received signal Fast Fourier Transform (FFT) and short-time Fourier transform (STFT) is used. This system is calibrated and range is calculated using a known Radar Cross-section (RCS) of the target.

Abstract: Corryvreckan is a versatile, highly configurable software with a modular structure designed to reconstruct and analyse test beam and laboratory data. It caters to the needs of the test beam community by providing a flexible offline event building facility to combine detectors with different read-out schemes, with or without trigger information, and includes the possibility to correlate data from multiple devices based on timestamps. Hit timing information, available with high precision from an increasing number of detectors, can be used in clustering and tracking to reduce combinatorics. Several algorithms, including an implementation of Millepede-II, are provided for offline alignment. A graphical user interface enables direct monitoring of the reconstruction progress and can be employed for quasi-online monitoring during data taking. This work introduces the Corryvreckan framework architecture and user interface, and provides a detailed overview of the event building algorithm. The reconstruction and analysis capabilities are demonstrated with data recorded at the DESY II Test Beam Facility using the EUDAQ2 data acquisition framework with an EUDET-type beam telescope, a Timepix3 timing reference, a fine-pitch planar silicon sensor with CLICpix2 readout and the AIDA Trigger Logic Unit. The individual steps of the reconstruction chain are presented in detail.

Abstract: In high energy physics and nuclear physics experiments, particularly the ones based on a particle accelerator, the data rate from the detector is usually in the order of terabytes per second. These high throughput data from detector front-end electronics (FEEs) need to be transmitted to the back-end computing farm for high-level event selection and building. A data acquisition (DAQ) system with the features of high density, scalable, and easily upgradeable is crucial to simplify the readout architecture of the whole experiment. This article will introduce the design of a generic high bandwidth PCIe card, which can be used as the important input–output card in a scalable DAQ system. It can factorize FEEs from data handling and reduce the amount of custom hardware in favor of scalable detector-independent commercial hardware and software. Besides the 48 channels of bidirectional high-speed fiber optical links with front ends, it also supports to synchronize with the experiment timing system and to fan-out the clock and trigger information with a fixed latency to the FEEs.