Showing papers on "Data acquisition published in 2017"

Comparison of six electromyography acquisition setups on hand movement classification tasks.

Abstract: Hand prostheses controlled by surface electromyography are promising due to the non-invasive approach and the control capabilities offered by machine learning. Nevertheless, dexterous prostheses are still scarcely spread due to control difficulties, low robustness and often prohibitive costs. Several sEMG acquisition setups are now available, ranging in terms of costs between a few hundred and several thousand dollars. The objective of this paper is the relative comparison of six acquisition setups on an identical hand movement classification task, in order to help the researchers to choose the proper acquisition setup for their requirements. The acquisition setups are based on four different sEMG electrodes (including Otto Bock, Delsys Trigno, Cometa Wave + Dormo ECG and two Thalmic Myo armbands) and they were used to record more than 50 hand movements from intact subjects with a standardized acquisition protocol. The relative performance of the six sEMG acquisition setups is compared on 41 identical hand movements with a standardized feature extraction and data analysis pipeline aimed at performing hand movement classification. Comparable classification results are obtained with three acquisition setups including the Delsys Trigno, the Cometa Wave and the affordable setup composed of two Myo armbands. The results suggest that practical sEMG tests can be performed even when costs are relevant (e.g. in small laboratories, developing countries or use by children). All the presented datasets can be used for offline tests and their quality can easily be compared as the data sets are publicly available.

Achieving Efficient and Secure Data Acquisition for Cloud-Supported Internet of Things in Smart Grid

Abstract: Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on ciphertext policy attribute-based encryption. Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access subtree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.

Completely contactless structural health monitoring of real‐life structures using cameras and computer vision

Abstract: Summary A newly developed, completely contactless structural health monitoring system framework based on the use of regular cameras and computer vision techniques is introduced for obtaining displacements and vibrations of structures, which are critical responses for performance-based design and evaluation of structures. To provide contactless and practical monitoring, the current vision-based displacement measurement methods are improved by eliminating the physical target attachment. This is achieved by means of utilizing imaging key-points as virtual targets. As a result, pixel-based displacements of a monitored structural location are determined by using an improved detection and match key-points algorithm, in which false matches are identified and discarded almost completely. To transform pixel-based displacements to engineering units, a practical camera calibration method is developed because calibration standard on a physical target no longer exists. Moreover, a framework for evaluating the accuracy of vision-based displacement measurements is established for the first time, which, in return, provides users with the most crucial information of a measurement. The proposed framework along with a conventional sensor network and a data acquisition system are applied and verified on a real-life stadium during football games for structural assessment. The results obtained by the new method are successfully validated with the data acquired from sensors such as linear variable differential transformers and accelerometers. Because the proposed method does not require any type of sensor and target attachment, common field works such as sensor installation, wiring, maintaining conventional data acquisition systems are not required. This advantage enables an inexpensive and practical way for structural assessment, especially for real-life structures. Copyright © 2016 John Wiley & Sons, Ltd.

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

Abstract: The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.

Condition monitoring approaches for the detection of railway wheel defects

Abstract: Condition monitoring systems are commonly exploited to assess the health status of equipment. A fundamental part of any condition monitoring system is data acquisition. Meaningfully estimating the current condition and predicting the future behaviour of the equipment strongly depend on the characteristic of the data measurement stage. Nowadays, condition monitoring has wide applications in the railway industry, and various monitoring approaches have been proposed for the inspection of wheel and rail conditions. In-service condition monitoring of wheels provides the real-time data required for maintenance planning, while in-workshop inspection is normally done at fixed intervals carried out periodically. In-service data acquisition can be divided into on-board and wayside measurements. In this paper, on the basis of these classifications, the existing data acquisition techniques for the monitoring of railway wheel condition are reviewed, and the state-of-the-art methods and required research are discussed.

IoT real time data acquisition using MQTT protocol

Abstract: The Internet of Things (IoT) provides ease to monitor and to gain sensor data through the Internet [1]. The need of high quality data is increasing to the extent that data monitoring and acquisition system in real time is required, such as smart city or telediagnostic in medical areas [2]. Therefore, an appropriate communication protocol is required to resolve these problems. Lately, researchers have developed a lot of communication protocols for IoT, of which each has advantages and disadvantages. This study proposes the utilization of MQTT as a communication protocol, which is one of data communication protocols for IoT. This study used temperature and humidity sensors because the physical parameters are often needed as parameters of environment condition [3]. Data acquisition was done in real-time and stored in MySQL database. This study is also completed by interface web-based and mobile for online monitoring. This result of this study is the enhancement of data quality and reliability using MQTT protocol.

UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line

Abstract: Regular inspection of transmission lines is an essential work, which has been implemented by either labor intensive or very expensive approaches. 3D reconstruction could be an alternative solution to satisfy the need for accurate and low cost inspection. This paper exploits the use of an unmanned aerial vehicle (UAV) for outdoor data acquisition and conducts accuracy assessment tests to explore potential usage for offsite inspection of transmission lines. Firstly, an oblique photogrammetric system, integrating with a cheap double-camera imaging system, an onboard dual-frequency GNSS (Global Navigation Satellite System) receiver and a ground master GNSS station in fixed position, is designed to acquire images with ground resolutions better than 3 cm. Secondly, an image orientation method, considering oblique imaging geometry of the dual-camera system, is applied to detect enough tie-points to construct stable image connection in both along-track and across-track directions. To achieve the best geo-referencing accuracy and evaluate model measurement precision, signalized ground control points (GCPs) and model key points have been surveyed. Finally, accuracy assessment tests, including absolute orientation precision and relative model precision, have been conducted with different GCP configurations. Experiments show that images captured by the designed photogrammetric system contain enough information of power pylons from different viewpoints. Quantitative assessment demonstrates that, with fewer GCPs for image orientation, the absolute and relative accuracies of image orientation and model measurement are better than 0.3 and 0.2 m, respectively. For regular inspection of transmission lines, the proposed solution can to some extent be an alternative method with competitive accuracy, lower operational complexity and considerable gains in economic cost.

Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

Abstract: The development of intelligent vehicle systems has resulted in an increased need for a high-precision road map. However, conventional road maps that are used for vehicle navigation systems or geographical information systems (GISs) are insufficient to satisfy new requirements of intelligent vehicle systems such as autonomous driving. There are three primary road-map requirements for intelligent vehicle systems: centimeter-level accuracy, storage efficiency, and usability. However, no existing researches have met these three requirements simultaneously. In this paper, we propose a precise and efficient lane-level road-map generation system that conforms to the requirements all together. The proposed map-building process consists of three steps: 1) data acquisition, 2) data processing, and 3) road modeling. The road data acquisition and processing system captures accurate 3-D road geometry data by acquiring data with a mobile 3-D laser scanner. The road geometry data are then refined to extract meta information, and in the road modeling system, the refined data are represented as sets of piecewise polynomials to ensure storage efficiency and usability of the map. The proposed mapping system has been extensively tested and evaluated on a real urban road and highway. The experimental results show that the proposed mapping system outperforms conventional systems in terms of the road-map requirements.

A multi-frequency electrical impedance tomography system for real-time 2D and 3D imaging.

Abstract: This paper presents the design and evaluation of a configurable, fast multi-frequency Electrical Impedance Tomography (mfEIT) system for real-time 2D and 3D imaging, particularly for biomedical imaging. The system integrates 32 electrode interfaces and the current frequency ranges from 10 kHz to 1 MHz. The system incorporates the following novel features. First, a fully adjustable multi-frequency current source with current monitoring function is designed. Second, a flexible switching scheme is developed for arbitrary sensing configuration and a semi-parallel data acquisition architecture is implemented for high-frame-rate data acquisition. Furthermore, multi-frequency digital quadrature demodulation is accomplished in a high-capacity Field Programmable Gate Array. At last, a 3D imaging software, visual tomography, is developed for real-time 2D and 3D image reconstruction, data analysis, and visualization. The mfEIT system is systematically tested and evaluated from the aspects of signal to noise ratio (SNR), frame rate, and 2D and 3D multi-frequency phantom imaging. The highest SNR is 82.82 dB on a 16-electrode sensor. The frame rate is up to 546 fps at serial mode and 1014 fps at semi-parallel mode. The evaluation results indicate that the presented mfEIT system is a powerful tool for real-time 2D and 3D imaging.

OXSA: An open-source magnetic resonance spectroscopy analysis toolbox in MATLAB

Abstract: In vivo magnetic resonance spectroscopy provides insight into metabolism in the human body. New acquisition protocols are often proposed to improve the quality or efficiency of data collection. Processing pipelines must also be developed to use these data optimally. Current fitting software is either targeted at general spectroscopy fitting, or for specific protocols. We therefore introduce the MATLAB-based OXford Spectroscopy Analysis (OXSA) toolbox to allow researchers to rapidly develop their own customised processing pipelines. The toolbox aims to simplify development by: being easy to install and use; seamlessly importing Siemens Digital Imaging and Communications in Medicine (DICOM) standard data; allowing visualisation of spectroscopy data; offering a robust fitting routine; flexibly specifying prior knowledge when fitting; and allowing batch processing of spectra. This article demonstrates how each of these criteria have been fulfilled, and gives technical details about the implementation in MATLAB. The code is freely available to download from https://github.com/oxsatoolbox/oxsa.

Forensic data acquisition from cloud-of-things devices: windows Smartphones as a case study

Abstract: Summary The continued amalgamation of cloud technologies into all aspects of our daily lives and the technologies we use (i.e. cloud-of-things) creates business opportunities, security and privacy risks, and investigative challenges (in the event of a cybersecurity incident). This study examines the extent to which data acquisition from Windows phone, a common cloud-of-thing device, is supported by three popular mobile forensics tools. The effect of device settings modification (i.e. enabling screen lock and device reset operations) and alternative acquisition processes (i.e. individual and combined acquisition) on the extraction results are also examined. Our results show that current mobile forensic tool support for Windows Phone 8 remains limited. The results also showed that logical acquisition support was more complete in comparison to physical acquisition support. In one example, the tool was able to complete a physical acquisition of a Nokia Lumia 625, but its deleted contacts and SMSs could not be recovered/extracted. In addition we found that separate acquisition is needed for device removable media to maximize acquisition results, particularly when trying to recover deleted data. Furthermore, enabling flight-mode and disabling location services are highly recommended to eliminate the potential for data alteration during the acquisition process. These results should provide practitioners with an overview of the current capability of mobile forensic tools and the challenges in successfully extracting evidence from the Windows phone platform. Copyright © 2016 John Wiley & Sons, Ltd.

3D printed bio-models for medical applications

Abstract: Purpose The design process of a bio-model involves multiple factors including data acquisition technique, material requirement, resolution of the printing technique, cost-effectiveness of the printing process and end-use requirements. This paper aims to compare and highlight the effects of these design factors on the printing outcome of bio-models. Design/methodology/approach Different data sources including engineering drawing, computed tomography (CT), and optical coherence tomography (OCT) were converted to a printable data format. Three different bio-models, namely, an ophthalmic model, a retina model and a distal tibia model, were printed using two different techniques, namely, PolyJet and fused deposition modelling. The process flow and 3D printed models were analysed. Findings The data acquisition and 3D printing process affect the overall printing resolution. The design process flows using different data sources were established and the bio-models were printed successfully. Research limitations/implications Data acquisition techniques contained inherent noise data and resulted in inaccuracies during data conversion. Originality/value This work showed that the data acquisition and conversion technique had a significant effect on the quality of the bio-model blueprint and subsequently the printing outcome. In addition, important design factors of bio-models were highlighted such as material requirement and the cost-effectiveness of the printing technique. This paper provides a systematic discussion for future development of an engineering design process in three-dimensional (3D) printed bio-models.

Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing

Abstract: As a significant business paradigm, data trading has attracted increasing attention. However, the study of data acquisition in data markets is still in its infancy. Mobile crowdsensing has been recognized as an efficient and scalable way to acquire large-scale data. Designing a practical data acquisition scheme for crowd-sensed data markets has to consider three major challenges: crowd-sensed data trading format determination, profit maximization with polynomial computational complexity, and payment minimization in strategic environments. In this paper, we jointly consider these design challenges, and propose VENUS, which is the first profit-driVEN data acqUiSition framework for crowd-sensed data markets. Specifically, VENUS consists of two complementary mechanisms: VENUS-PRO for profit maximization and VENUS-PAY for payment minimization. Given the expected payment for each of the data acquisition points, VENUS-PRO greedily selects the most “cost-efficient” data acquisition points to achieve a sub-optimal profit. To determine the minimum payment for each data acquisition point, we further design VENUS-PAY, which is a data procurement auction in Bayesian setting. Our theoretical analysis shows that VENUS-PAY can achieve both strategy-proofness and optimal expected payment. We evaluate VENUS on a public sensory data set, collected by Intel Research, Berkeley Laboratory. Our evaluation results show that VENUS-PRO approaches the optimal profit, and VENUS-PAY outperforms the canonical second-price reverse auction, in terms of total payment.

Design and deployment of a smart system for data gathering in aquaculture tanks using wireless sensor networks

Abstract: Summary The design of monitoring systems for marine areas has increased in the last years One of the many advantages of wireless sensor networks is the quick process in data acquisition The information from sensors can be processed, stored, and transmitted using protocols efficiently designed to energy saving and establishing the fastest routes The processing and storing of data can be very useful for taking intelligent decisions for improving the water quality The monitoring of water exchange in aquaculture tanks is very important to monitor the fish welfare Thus, this paper presents the design, deployment, and test of a smart data gathering system for monitoring several parameters in aquaculture tanks using a wireless sensor network The system based on a server is able to request and collect data from several nodes and store them in a database This information can be postprocessed to take efficient decisions The paper also presents the design of a conductivity sensor and a level sensor These sensors are installed in several aquaculture tanks The system was implemented using Flyport modules Finally, the data gathering system was tested in terms of consumed bandwidth and the delay Transmission Control Protocol (TCP) packets delivering data from the sensors

Compressed Sensing and Parallel Acquisition

Abstract: Parallel acquisition systems arise in various applications to moderate problems caused by insufficient measurements in single-sensor systems. These systems allow simultaneous data acquisition in multiple sensors, thus alleviating such problems by providing more overall measurements. In this paper, we consider the combination of compressed sensing with parallel acquisition. We establish the theoretical improvements of such systems by providing nonuniform recovery guarantees for which, subject to appropriate conditions, the number of measurements required per sensor decreases linearly with the total number of sensors. Throughout, we consider two different sampling scenarios—distinct (i.e., independent sampling in each sensor) and identical (i.e., dependent sampling between sensors)—and a general mathematical framework that allows for a wide range of sensing matrices. We also consider not just the standard sparse signal model, but also the so-called sparse in levels signal model. As our results show, optimal recovery guarantees for both distinct and identical sampling are possible under much broader conditions on the so-called sensor profile matrices (which characterize environmental conditions between a source and the sensors) for the sparse in levels model than for the sparse model. To verify our recovery guarantees, we provide numerical results showing phase transitions for different multi-sensor environments.

Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation

Abstract: In this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches.

GPS-Disciplined Analog-to-Digital Converter for Phasor Measurement Applications

Abstract: This paper presents a data acquisition unit which synchronously samples multiple channels in a manner such that the time of day at which each sample is taken is known. This allows measurements taken at multiple locations to be compared with confidence. The intended application is wide area electrical power system measurements, in particular phasor measurement units (PMUs). The novelty of the authors’ design is the application of an open hardware development platform to discipline a commodity analog-to-digital converter (ADC) to a broadcast time signal, usually but not exclusively GPS. The methodology used creates a driver layer for the ADC to achieve real-time sampling in a nonpreemptive Linux environment. The use of open hardware and software addresses the need for a transparent instrument for use in research and development of PMU technology. Through a choice of either a software or hardware phase-locked loop, the ADC is controlled to acquire exactly 256 samples per nominal power system cycle (i.e., 50/60 Hz), precisely time synchronized to GPS, at 16-b resolution and 94.2-dB SNR. The design of a printed circuit board expansion board featuring all necessary components is provided. The performance of the system is evaluated. Interoperability and data exchange with other systems is achieved by use of open schemas and communication protocols. This allows rapid integration with popular numerical simulation environments.

Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI).

Abstract: The ability to experimentally perturb biological systems has traditionally been limited to static pre-programmed or operator-controlled protocols. In contrast, real-time control allows dynamic probing of biological systems with perturbations that are computed on-the-fly during experimentation. Real-time control applications for biological research are available; however, these systems are costly and often restrict the flexibility and customization of experimental protocols. The Real-Time eXperiment Interface (RTXI) is an open source software platform for achieving hard real-time data acquisition and closed-loop control in biological experiments while retaining the flexibility needed for experimental settings. RTXI has enabled users to implement complex custom closed-loop protocols in single cell, cell network, animal, and human electrophysiology studies. RTXI is also used as a free and open source, customizable electrophysiology platform in open-loop studies requiring online data acquisition, processing, and visualization. RTXI is easy to install, can be used with an extensive range of external experimentation and data acquisition hardware, and includes standard modules for implementing common electrophysiology protocols.

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection.

Abstract: Wearable technology has gained increasing popularity in the applications of healthcare, sports science, and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the (surface electromyography) sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality, and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition, and fatigue tracking. To this end, we introduce an embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multichannel data acquisition unit. For the first stage, we propose a low cost, dry, and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multichannel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used commercially available product and our data acquisition system achieves 4.583 dB SNR gain with $98.9784\%$ accuracy in the detection of the contractions.

Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources

Abstract: Online data acquisition, data assimilation and integrated hydrological modelling have become more and more important in hydrological science. In this study, we explore cloud computing for integrating field data acquisition and stochastic, physically-based hydrological modelling in a data assimilation and optimisation framework as a service to water resources management. For this purpose, we developed an ensemble Kalman filter-based data assimilation system for the fully-coupled, physically-based hydrological model HydroGeoSphere, which is able to run in a cloud computing environment. A synthetic data assimilation experiment based on the widely used tilted V-catchment problem showed that the computational overhead for the application of the data assimilation platform in a cloud computing environment is minimal, which makes it well-suited for practical water management problems. Advantages of the cloud-based implementation comprise the independence from computational infrastructure and the straightforward integration of cloud-based observation databases with the modelling and data assimilation platform. A cloud-based real-time modelling and data assimilation framework is established.Can be connected to a cloud-based data acquisition and monitoring module.HydroGeoSphere is used as the hydrological forward model.The tiltedV-catchment problem is used as a benchmark for the system.

Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs.

Abstract: In recent years, UAVs (Unmanned Aerial Vehicles) have been widely applied for data collection and image capture. Specifically, UAVs have been integrated with wireless sensor networks (WSNs) to create data collection platforms with high flexibility. However, most studies in this domain focus on system architecture and UAVs’ flight trajectory planning while event-related factors and other important issues are neglected. To address these challenges, we propose a cloud-assisted data gathering strategy for UAV-based WSN in the light of emerging events. We also provide a cloud-assisted approach for deriving UAV’s optimal flying and data acquisition sequence of a WSN cluster. We validate our approach through simulations and experiments. It has been proved that our methodology outperforms conventional approaches in terms of flying time, energy consumption, and integrity of data acquisition. We also conducted a real-world experiment using a UAV to collect data wirelessly from multiple clusters of sensor nodes for monitoring an emerging event, which are deployed in a farm. Compared against the traditional method, this proposed approach requires less than half the flying time and achieves almost perfect data integrity.

Threat early warning and monitoring system and method based on big data analysis and deployment architecture

Abstract: The invention discloses a threat early warning and monitoring system and method based on big data analysis and a deployment architecture The system comprises a data acquisition system module, which is used for carrying out real-time data acquisition on original network traffic; a data storage system module, which is used for carrying out data merging and data cleaning on the data collected by the data acquisition system module, and then, carrying out storage management; a real-time threat intelligent analysis system module, which is used for carrying out deep analysis and mining on security data through data mining, text analysis, traffic analysis, full-text search engine and real-time processing, and identifying unknown security threats in real time by combining an intrusion detection module, a network abnormal behavior module and a device abnormal behavior module; and a situation awareness display system module, which is used for carrying out comprehensive display on security threat situations stereoscopically in real time through a data visualization tool library The threat early warning and monitoring system and method based on big data analysis and the deployment architecture are used for network security threat situation awareness and deep analysis under a plurality of service scenarios, and realize comprehensive abilities from attack early warning, attack identification to analysis and evidence obtaining