Hybrid Acquisition System Research Articles

A Novel Hybrid Acquisition System for Industrial Condition Monitoring and Predictive Maintenance

A Novel Hybrid Acquisition System for Industrial Condition Monitoring and Predictive Maintenance

Development of a power station unit in a distributed hybrid acquisition system of seismic and electrical methods based on the narrowband Internet of Things (NB-IoT)

Abstract. In this paper, we propose a new type of power station unit with wireless data transmission capability. This work breaks the limitation that conventional equipment is unable to upload data directly to a central unit. Based on that, a novel distributed geophysical data acquisition architecture is also proposed, enhancing the work efficiency by simplifying the system structure while maintaining core features. Designs that realise key functions including isolated high-power output, power management, wireless data transmission and high-precision clock synchronisation are introduced in this article. The prototype was packaged then, and a series of evaluation experiments were implemented to verify the key parameters of the instrument. Experiment results proved that the overall design of the instrument is feasible, and the key parameters outperform the industry leading instrument LAUL-428. Due to the wireless networking strategy, the proposed instrument further realises remote control and real-time data playback through the host computer software, making it suitable for joint geophysical exploration as well as microseismic monitoring. As for the system level, it could be customised by connecting different kinds of conventional acquisition stations for many kinds of prospecting targets.

Simulating a Hybrid Acquisition System for UAV Platforms

Currently, there is a rapid trend in the production of airborne sensors consisting of multi-view cameras or hybrid sensors, i.e., a LiDAR scanner coupled with one or multiple cameras to enrich the data acquisition in terms of colors, texture, completeness of coverage, accuracy, etc. However, the current UAV hybrid systems are mainly equipped with a single camera that will not be sufficient to view the facades of buildings or other complex objects without having double flight paths with a defined oblique angle. This entails extensive flight planning, acquisition duration, extra costs, and data handling. In this paper, a multi-view camera system which is similar to the conventional Maltese cross configurations used in the standard aerial oblique camera systems is simulated. This proposed camera system is integrated with a multi-beam LiDAR to build an efficient UAV hybrid system. To design the low-cost UAV hybrid system, two types of cameras are investigated and proposed, namely the MAPIR Survey and the SenseFly SODA, integrated with a multi-beam digital Ouster OS1-32 LiDAR sensor. Two simulated UAV flight experiments are created with a dedicated methodology and processed with photogrammetric methods. The results show that with a flight speed of 5 m/s and an image overlap of 80/80, an average density of up to 1500 pts/m2 can be achieved with adequate facade coverage in one-pass flight strips.

Deep Task-Based Analog-to-Digital Conversion

Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. Their conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-amplitude quantities using a finite number of bits. ADCs typically implement generic uniform conversion mappings that are ignorant of the task for which the signal is acquired, and can be costly when operating in high rates and fine resolutions. In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out. We propose a model for sampling and quantization that facilitates the learning of non-uniform mappings from data. Based on this learnable ADC mapping, we present a mechanism for optimizing a hybrid acquisition system comprised of analog combining, tunable ADCs with fixed rates, and digital processing, by jointly learning its components end-to-end. Then, we show how one can exploit the representation of hybrid acquisition systems as deep networks to optimize the sampling and quantization rates given the task by utilizing Bayesian meta-learning techniques. We evaluate the proposed deep task-based ADC in two case studies: the first considers synthetic multi-variate symbol detection, where multiple analog signals are simultaneously acquired in order to recover a set of discrete symbols. The second application is beamforming of analog channel data acquired in ultrasound imaging. Our numerical results demonstrate that the proposed approach achieves performance which is comparable to operating with high sampling rates and fine resolution quantization, while operating with reduced overall bit rate. For instance, we demonstrate that deep task-based ADCs enable accurate reconstruction of ultrasound images while using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$12.5\%$</tex-math></inline-formula> of the overall number of bits used by conventional ADCs.

Making seismic monitoring work in a complex desert environment — 4D processing

Seismic monitoring of an onshore carbonate reservoir in a desert environment has been achieved for the first time. Optimizing data repeatability was key to detecting the weak 4D (time-lapse) signal resulting from a fluid-injection program, which was achieved through a combination of specialized survey design, careful acquisition, and dedicated 4D processing. The hybrid acquisition system utilized buried geophones, which significantly reduced 4D noise caused by variability in the near-surface environment. Despite the extensive acquisition efforts, time-lapse processing is an essential component of achieving highly repeatable data. A fit-for-purpose workflow was developed to reduce the remaining 4D noise using a combination of parallel and simultaneous processing. Processing steps leading to the largest improvement in reflection signal-to-noise ratio, such as noise attenuation, amplitude balancing, and supergrouping, produced the largest reduction in 4D noise. Outstanding final migrated data repeatability has been achieved, comparable to levels reported for the more favorable permanent marine systems. However, the need to use surface sources results in a seasonal imprint on data repeatability, which hinders the interpretation of surveys acquired during different seasons. In the absence of a fully buried acquisition system, advanced processing techniques such as surface-consistent matching filters may be required to resolve these variations.

GPU accelerated real-time multi-functional spectral-domain optical coherence tomography system at 1300nm

We present a GPU accelerated multi-functional spectral domain optical coherence tomography system at 1300nm. The system is capable of real-time processing and display of every intensity image, comprised of 512 pixels by 2048 A-lines acquired at 20 frames per second. The update rate for all four images with size of 512 pixels by 2048 A-lines simultaneously (intensity, phase retardation, flow and en face view) is approximately 10 frames per second. Additionally, we report for the first time the characterization of phase retardation and diattenuation by a sample comprised of a stacked set of polarizing film and wave plate. The calculated optic axis orientation, phase retardation and diattenuation match well with expected values. The speed of each facet of the multi-functional OCT CPU-GPU hybrid acquisition system, intensity, phase retardation, and flow, were separately demonstrated by imaging a horseshoe crab lateral compound eye, a non-uniformly heated chicken muscle, and a microfluidic device. A mouse brain with thin skull preparation was imaged in vivo and demonstrated the capability of the system for live multi-functional OCT visualization.

Impact of GPS acquisition strategy on decision probabilities

The first stage of processing within a GPS receiver consists of the signal acquisition process, the output of which provides a rough estimation of code delay and Doppler frequency. The strong dependence of the acquisition performance on the decision strategy is shown, establishing the role of decision probabilities. Three acquisition strategies are analyzed and a new model describing the performance of a hybrid acquisition system is developed. The theoretical models are validated by simulations, and secondary phenomena, generally neglected in the literature, are also discussed.

Calibration of an orientation sensor for freehand 3D ultrasound and its use in a hybrid acquisition system

BackgroundFreehand 3D ultrasound is a powerful imaging modality with many potential applications. However, its reliance on add-on position sensors, which can be expensive, obtrusive and difficult to calibrate, is a major drawback. Alternatively, freehand 3D ultrasound can be acquired without a position sensor using image-based techniques. Sensorless reconstructions exhibit good fine scale detail but are prone to tracking drift, resulting in large scale geometrical distortions.MethodWe investigate an alternative position sensor, the Xsens MT9-B, which is relatively unobtrusive but measures orientation only. We describe a straightforward approach to calibrating the sensor, and we measure the calibration precision (by repeated calibrations) and the orientation accuracy (using independent orientation measurements). We introduce algorithms that allow the MT9-B potentially to correct both linear and angular drift in sensorless reconstructions.ResultsThe MT9-B can be calibrated to a precision of around 1°. Reconstruction accuracy is also around 1°. The MT9-B was able to eliminate angular drift in sensorless reconstructions, though it had little impact on linear drift. In comparison, six degree-of-freedom drift correction was shown to produce excellent reconstructions.ConclusionGold standard freehand 3D ultrasound acquisition requires the synthesis of image-based techniques, for good fine scale detail, and position sensors, for good large scale geometrical accuracy. A hybrid system incorporating the MT9-B offers an attractive compromise between quality and ease of use. The position sensor is unobtrusive and the system is capable of faithful acquisition, with the one exception of linear drift in the elevational direction.

Statistical Evaluation of Adaptive Threshold Alignment for a Multi-Carrier CDMA Code Synchronization

Summary This letter presents a statistical analysis of the decision threshold for a multi-carrier (MC) DS/CDMA acquisition system with a reference filter. The probabilities of detection and false alarm are derived, and the mean acquisition time is evaluated as a measure of the system performance. From the results, it is shown that in the performance analysis of the hybrid acquisition system with reference filtering, the statistical evaluation of the decision threshold seems more appropriate than the approximation of the decision threshold adopted in other schemes [7,8].