Traditional Acquisition Methods Research Articles

Real-time signal acquisition based on optical excitable response by pre-designing single-tone preamble waveform.

Real-time acquisition of target signals is preferred for mobile communication systems. However, under the requirement of ultra-low latency for next-generation communication, traditional acquisition methods need to temporally locate the target signal from a large amount of raw data with correlation-based computing, introducing extra latency. We propose a real-time signal acquisition method based on an optical excitable response (OER) by pre-designing a single-tone preamble waveform. The preamble waveform is designed to be within the amplitude and bandwidth of the target signal, so no extra transceiver is required. The OER generates a corresponding pulse to the preamble waveform in the analog domain, which simultaneously triggers an analog-to-digital converter (ADC) to acquire target signals. The dependence of OER pulse on the preamble waveform parameter is studied, leading to a pre-design of the preamble waveform for an optimal OER. In the experiment, we demonstrate a millimeter-wave (26.5-GHz) transceiver system with target signals of orthogonal frequency division multiplexing (OFDM) format. Experimental results show that the response time is less than 4 ns, which is far lower than the ms-level response time of traditional all-digital time-synchronous acquisition methods.

Design of Multichannel Multirate Signal Acquisition System Based on FPGA

With the continuous advancement of science and technology, the fields of national defense, astronomy, industry, agriculture, and so on often require real-time acquisition of voltage, current, temperature, pressure, flow, and other signals, and the real-time requirements for signal acquisition are getting higher and higher. The traditional temperature acquisition method is to use the single chip or DSP as the control core to design the temperature monitoring system. However, this method has certain defects and cannot meet many high-demand signal acquisition occasions. On the one hand, most functions of this system are realized by software, and the operation of software will occupy part of the system sampling time, thus reducing the system sampling rate; on the other hand, the logic of the system to complex peripheral circuits cannot be well controlled. And Field Programmable Gate Array (FPGA) has characteristics such as parallel processing, controllability, and processing speed; therefore, the signal acquisition system with FPGA as the control core can realize high-speed signal collection. This paper studies the signal acquisition based on FPGA. In the signal acquisition system, FPGA is the control core of the whole system, and the relevant function modules of the control system complete the signal acquisition task. The acquisition control module is mainly to control the channel selection switch and the A/D converter to work, that is, to generate the logic control signal of multichannel selection and the control sequence of A/D conversion, so as to complete the acquisition and control of the multichannel analog input signal. The FPGA-based signal acquisition control system has the characteristics of short development cycle, flexible design, low cost, and high reliability. FPGA is an ideal architecture for signal acquisition, and the FPGA-based signal acquisition technology has very broad application prospects.

Extraction of Hidden Science from Nanoscale Images.

Scanning probe microscopies and spectroscopies enable investigation of surfaces and even buried interfaces down to the scale of chemical-bonding interactions, and this capability has been enhanced with the support of computational algorithms for data acquisition and image processing to explore physical, chemical, and biological phenomena. Here, we describe how scanning probe techniques have been enhanced by some of these recent algorithmic improvements. One improvement to the data acquisition algorithm is to advance beyond a simple rastering framework by using spirals at constant angular velocity then switching to constant linear velocity, which limits the piezo creep and hysteresis issues seen in traditional acquisition methods. One can also use image-processing techniques to model the distortions that appear from tip motion effects and to make corrections to these images. Another image-processing algorithm we discuss enables researchers to segment images by domains and subdomains, thereby highlighting reactive and interesting disordered sites at domain boundaries. Lastly, we discuss algorithms used to examine the dipole direction of individual molecules and surface domains, hydrogen bonding interactions, and molecular tilt. The computational algorithms used for scanning probe techniques are still improving rapidly and are incorporating machine learning at the next level of iteration. That said, the algorithms are not yet able to perform live adjustments during data recording that could enhance the microscopy and spectroscopic imaging methods significantly.

Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping

Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one hour dwell time and no GCPs.

Assessing the influence of environmental factors and datasets on soil type prediction with two machine learning algorithms in a heterogeneous area in the Rur catchment, Germany

Machine Learning (ML) algorithms are a promising alternative to traditional acquisition methods for creating new or updating existing soil maps. This study analyses the suitability of two ML techniques for the prediction of 36 different soil types in the Rur catchment in North-Rhine Westphalia (Germany). For this purpose, the performance of random forest (RF) and artificial neural network (ANN) classifiers have been investigated for three different scenarios with varying environmental co-variables for prediction and two varying training datasets with different sampling strategies. It has been analysed how the accuracy of classified digital soil map products is affected by the diversity of available soil types within different landscapes of the catchment, by varying topography, as well as different spatial resolutions of the co-variables and the distribution of training points. Co-variables derived from a digital elevation model (DEM) were once generated with a high-resolution DEM from airborne laser scanning data in a spatial resolution of 15 m and once with the 90 m TanDEM-X WorldDEMtm. Results generally show best performance for the RF classification with overall accuracies (OA) over 70% with a spatially homogenized training dataset. The ANN classifier performed on average about 5% lower compared to RF. Furthermore, it could be shown for both algorithms that the OA is about 15% - 25% lower for areas in the northernmost and central part of the study area with a very diverse distribution of soil types, compared to other regions with only a few dominating soil types. Particularly for the ANN classifier with spatially homogenized training samples the observed drop in accuracy was considerably high for heterogeneous regions. A comparison of different predictor variables from different DEM sources with greatly varying spatial resolutions showed similar results for both datasets and an increase of accuracy with higher spatial resolutions could not be detected here. Overall, the classification accuracy is mainly affected by the sampling strategy of training samples, the diversity of distributed soil types and the availability of predictive environmental co-variables. In contrast, influence of topography and spatial resolution of DEM for the generation of predictor variables was only minor.

The Acquisition Method of Symmetry Chirp Signal Used in LEO Satellite Internet of Things

Chirp signal has been used in some fields, like radar and sonar. In recent years, the symmetry chirp signal (SCS) is a kind of revised Long Range (LoRa) chirp signal and proposed to be used in low-earth-orbit (LEO) satellite Internet of Things (IoT) for low-data-rate transmission. However, in terms of the complexity and performance, the traditional acquisition methods to capture SCS (time delay and frequency offset acquisition) will have some limitations in LEO satellite IoT. Thus, this letter presents a new acquisition method to balance the complexity and performance for SCS.

Development and Analysis of an Enhanced Multi-Expert Knowledge Integration System for Designing Context-Aware Ubiquitous Learning Contents

This article describes how in context-aware ubiquitous learning environments, teachers must plan a theme and design learning contents to provide complete knowledge for students. Knowledge acquisition, which is an approach for helping people represent and organize domain knowledge, has been recognized as a potential way of guiding teachers to develop real-world context-related learning contents. However, previous studies failed to address the issue that the learning contents provided by multiple experts or teachers might be redundant or inconsistent; moreover, it is difficult to use the traditional knowledge acquisition method to fully describe the complex real-world contexts and the learning contents. Therefore, in this article, a multi-expert knowledge integration system with an enhanced knowledge representation approach and Delphi method has been developed. From the experimental results, it is found that the teachers involved had a high degree of acceptance of the system. They believe that it can unify the knowledge of many teachers.

Single Pixel Compressive Spectral Polarization Imaging using a Movable Micro-Polarizer Array

The acquisition of spectral polarization images is a method that obtains polarized, spectral and spatial information of a scene. Traditional acquisition methods use dynamic elements that capture all the information of a scene, by scanning the areas of interest, which result in large amounts of data proportional to the desired image resolution. Hence, in this work, the compression of spectral polarization images using a single pixel architecture, that uses a micro-polarizer array aligned with a binary coded aperture is proposed. The micro-polarizer is moved horizontally in each shot, so that diverse types of codifications from the scene are obtained. The proposed architecture allows several compressive 2D projections with spatial, spectral and polarization coding to be obtained. Stokes parameter images at several wavelengths are reconstructed. This architecture reduces the total number of measurements needed to obtain spectral polarization images compared to traditional acquisition methods. The experiments validate the quality of the architecture obtaining 43.19 dB, 37.49 dB and 30.41 dB of the peak signal-to-noise ratio for the first three Stokes parameters respectively.

Compressive Data Gathering Based on Even Clustering for Wireless Sensor Networks

Compressive data gathering (CDG) based on compressed sensing (CS) theory for wireless sensor networks (WSNs) greatly reduces the amount of data transmitted compared with the traditional acquisition method that each node forwards the collected data directly to the next node. CDG combined with sparse random projection can further reduce the amount of data and thus prolong the lifetime of the WSN. The method of randomly selecting projection nodes as cluster heads to collect the weighted sum of sensor nodes outperforms the non-CS (without using CS) and hybrid-CS (applying CS only to relay nodes that are overloaded) schemes in decreasing the communication cost and distributing the energy consumption loads. However, the random selection of projection nodes causes the overall energy consumption of the network to be unstable and unbalanced. In this paper, we propose two compressive data gathering methods of balanced projection nodes. For WSN with uniform distribution of nodes, an even clustering method based on spatial locations is proposed to distribute the projection nodes evenly and balance the network energy consumption. For WSN with unevenly distributed nodes, an even clustering method based on node density is proposed, taking into account the location and density of nodes together, balancing the network energy and prolonging the network lifetime. The simulation results show that compared with the random projection node method and the random walk method, our proposed methods have better network connectivity and more significantly increased overall network lifetime.

A GNSS Acquisition Method with the Capability of Spoofing Detection and Mitigation

For Global navigation satellite system (GLASS), spoofing attack is extremely dangerous and destructive since it can lead GLASS receivers to generate misleading time and position information. Thus, reliable and timely spoofing detection and mitigation methods are of great importance. A new GLASS acquisition method with the capability of spoofing detection and mitigation is proposed in the paper. The proposed method can not only perform acquisition, but also detect and mitigate spoofing signals based on joint consistency detection of code and carrier Dopplers. We provide performance analysis and numerical simulation results to demonstrate the validity of the proposed method when there are one or even more spoofing signals. Successful acquisition probability of the authentic signal is compared to that achieved by traditional acquisition method. The proposed method can be easily implemented with only a little modification of current receiver acquisition module.

Weak DSSS signal acquisition with fine Doppler frequency estimation

An efficient acquisition method is proposed for high sensitivity and fine Doppler frequency estimation for direct-sequence spread spectrum signals. The proposed method first refines the Doppler frequency after a parallel code-phase search via triangle fitting of the cross-ambiguity function. Then, the information of amplitude and frequency before and after the frequency refining is used to exclude the false alarm. Compared to traditional acquisition methods, the proposed method has higher sensitivity and Doppler frequency estimation accuracy. Simulation results verify the validity of the proposed method.

Long‐term integration based on two‐stage differential acquisition for weak direct sequence spread spectrum signal

Fast acquisition in space telemetry and telecontrol communication system is challenged significantly by the high dynamic and low signal-to-noise ratio. Increasing the dwell time to obtain better acquisition sensitivity, based on two-dimensional time-frequency search, is impossible because of resource limitation in receiver. To tackle these problems, this study proposes a two-stage differential acquisition (TSDA) algorithm. The differential coherent integration is employed by the first stage of TDSA to extend integration time, during which keystone transform and fast Fourier transform are used to compensate the linear code phase drift and obtain high accumulation gain. The carrier-frequency offset is estimated in terms of the code phase difference between the two parts of received signal divided by the second stage of TSDA in which the two-dimensional searching process is avoided. The proposed TSDA algorithm and the traditional acquisition methods are compared under the same acquisition time and typical parameters of S-band space telemetry and telecontrol communication system. Simulation results demonstrate that the proposed TSDA algorithm attains a higher acquisition probability and better acquisition sensitivity than the traditional acquisition methods.

Compressively Sampled MRI Recovery Using Modified Iterative-Reweighted Least Square Method

Magnetic resonance imaging (MRI) is a medical imaging modality used for high-resolution soft-tissue imaging of human body. In traditional MRI acquisition methods, sampling is performed at Nyquist rate to store data in k-space. The MR image is recovered using inverse Fast Fourier Transform (FFT). This approach results in slow data acquisition process, which is uncomfortable for the patients. Compressed Sensing (CS) acquisition approach offers nearly perfect recovery of MR image using non-linear reconstruction algorithms even from partial k-space data. This study presents a novel method to reconstruct MR image from highly under-sampled data using modified Iterative-Reweighted Least Square (IRLS) method with additional data consistency constraints. IRLS is an effective numerical method used in convex optimization problems. The proposed algorithm was applied on original human brain and Shepp–Logan phantom image, and the data acquired from the MRI scanner at St. Mary’s Hospital, London. The experimental results show that the proposed algorithm outperforms Projection onto Convex Sets (POCS), Separable Surrogate Functional (SSF), Iterative-Reweighted Least Squares (IRLS), Zero Filling (ZF), and Low-Resolution (LR) methods based on the parameters, e.g. Peak Signal-to-Noise Ratio (PSNR), Improved Signal-to-Noise Ratio (ISNR), Fitness, Correlation, Structural SIMilarity (SSIM) index, and Artifact Power (AP).

MO-FG-202-03: Efficient Data Collection of Continuous 2D and Discrete Relative Dosimetric Data for Annual LINAC QA Using TrueBeam Developer Mode and a 1D Scanning Tank

Purpose: To develop a method to exploit real-time dynamic machine and couch parameter control during linear accelerator (LINAC) beam delivery to facilitate efficient performance of TG-142 suggested, Annual LINAC QA tests. Methods: Varian's TrueBeam Developer Mode (Varian Medical Systems, Palo Alto, CA) facilitates control of Varian's TrueBeam LINAC via instructions provided in Extensible Markup Language (XML) files. This allows machine and couch parameters to be varied dynamically, in real-time, during beam delivery. Custom XML files were created to allow for the collection of (1) continuous Tissue Maximum Ratios (TMRs), (2) beam profiles, and (3) continuous output factors using a 1D-scanning tank. TMRs were acquired by orienting an ionization chamber (IC) at isocenter (depth=25cm) and synchronizing a depth scan towards the water surface while lowering the couch at 1mm/s. For beam profiles, the couch was driven laterally and longitudinally while logging IC electrometer readings. Output factors (OFs) where collected by continually varying field sizes (4×4 to 30×30-cm2) at a constant speed of 6.66 mm/s. To validate measurements, comparisons were made to data collected using traditional methods (e.g. 1D or 3D tank). Results: All data collecting using the proposed methods agreed with traditionally collected data (TMRs within 1%, OFs within 0.5% and beam profile agreement within 1% / 1mm) while taking less time to collect (factor of approximately 1/10) and with a finer sample resolution. Conclusion: TrueBeam developer mode facilitates collection of continuous data with the same accuracy as traditionally collected data with a finer resolution in less time. Results demonstrate an order of magnitude increase in sampled resolution and an order of magnitude reduction in collection time compared to traditional acquisition methods (e.g. 3D scanning tank). We are currently extending this approach to perform other TG-142 tasks.

Modeling an Interwoven Collimator for A 3D Endocavity Gamma Camera

Positron emission tomography (PET) and single-photon emission-computed tomography (SPECT) are important nuclear-medical imaging tools in diagnosing cancers and creating effective treatment plans. Commercially imaging systems are operated externally and can create 3D images of the whole body or of specific organs by rotating the gamma-ray detectors, and then employing software to reconstruct the 3D images from the multiple 2D projections at different angles of view. However, their uses in intraoperative environments or for imaging specific small organs, e.g., the prostate, ovary, and cervix, are limited because of their bulky designs and the long working-distance, hence causing low efficiency and poor spatial-resolution. In such situations, compact imaging devices, e.g., the trans-rectal gamma camera developed at Brookhaven National Laboratory (BNL) and Hybridyne Imaging Technologies, are preferable for detecting intra-prostatic tumors. The camera uses pixilated cadmium zinc telluride (CdZnTe) detectors with a matched parallel-hole collimator. However, their lack of 3D imaging capability limits their use in clinics, because the acquired images cannot be interpreted easily due to missing depth information. Given the constraint on space in such operations, the traditional 3D-image acquisition methods are impractical. For this reason, we designed an interwoven collimator dedicated for 3D imaging using an endocavity probe. This novel collimator allows us to take two or multiple views of a specific organ or tissue without rotating the camera. At the first stage of design for the collimator, we carried out Monte-Carlo simulations to study the response of the collimator and the attached detectors to gamma rays, and then developed a maximum-likelihood-based algorithm for reconstructing 3D images. In this paper, we detail our modeling of the collimator on a cluster Linux computer, and discuss the imaging capability of this novel collimator.

Unmanned aerial systems for photogrammetry and remote sensing: A review

We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and Remote Sensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remote sensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last five years, these two sister disciplines have developed technology and methods that challenge the current aeronautical regulatory framework and their own traditional acquisition and processing methods. Navety and ingenuity have combined off-the-shelf, low-cost equipment with sophisticated computer vision, robotics and geomatic engineering. The results are cm-level resolution and accuracy products that can be generated even with cameras costing a few-hundred euros. In this review article, following a brief historic background and regulatory status analysis, we review the recent unmanned aircraft, sensing, navigation, orientation and general data processing developments for UAS photogrammetry and remote sensing with emphasis on the nano-micro-mini UAS segment.

A Fast Acquisition Algorithm Overcoming Fuzz Problems for TDDM Spread Spectrum Signal

TDDM (time division data modulation) technique will be used in the next generation GNSS (global navigation satellite system) to improve processing performance and to reduce inter-GNSS interference; however, the emergence of TDDM signal causes the estimation frequency and message reversal fuzz problems in the acquisition process of a GNSS receiver. At present, the traditional acquisition methods have some limitations and shortcomings. Therefore, aiming at the unique characteristics of TDDM signal, a fast acquisition algorithm is proposed to overcome these fuzz problems in this paper. In the proposed algorithm, three stages are obtained by some key technologies, which are theI-Qfrequency compensation, superposition processing, subsection processing, and reversion position estimation. Besides, the algorithm is simulated from carrier frequency error, code phase error, message inversion error, and processing speed. Theoretical and simulation results show that the new algorithm can quickly overcome the fuzz problems, and the new algorithm is better than the existing algorithm in the speed and accuracy, which demonstrates that this new algorithm is an effective search scheme for the next generation GNSS signals.

High-sensitive GPS signal acquisition method based on wavelet filtering

With regard to the low operation efficiency of Global Positioning System(GPS) weak signal high-sensitive acquisition algorithm,a new acquisition method applying wavelet transform and synchronized data blocks accumulation was proposed.The sampled Intermediate Frequency(IF) signal was processed using discrete wavelet transform and Signal-to-Noise Ratio(SNR) was improved by the different characteristics of useful signal and noise during wavelet transform.Meanwhile the quantity of baseband data was decreased.Frequency compensation and synchronized data blocks accumulation method were adopted to reduce the Doppler search range and improve SNR effectively.The simulation results show that compared with traditional high-sensitive acquisition method,the proposed method can obviously reduce the acquisition time and improve the performance of weak signal acquisition.

An FFT-Based Approach for Carrier Frequency Domain Acquisition in Spread Spectrum TT&C System

Acquisition time of precision ranging code is long under the conditions of large Doppler shift, the carrier frequency domain FFT-based fast acquisition theory was analyzed, and the method of carrier Doppler compensation estimates to complete the PN code Doppler compensation was directly used in spread spectrum ranging signal acquisition. Comparing with the traditional parallel acquisition approach, the Doppler frequency estimation accuracy and acquisition speed is greatly improved, the acquisition time is shorter than long code segment with the traditional parallel acquisition method. Comparing with the method of short code supporting for long code, the channel energy is saved.

Enhanced visualization of histological samples with an adjustable RGB contrast system with application for tissue used in photodynamic therapy

The analysis of histological sections has long been a valuable tool in the pathological studies. The interpretation of tissue conditions, however, relies directly on visual evaluation of tissue slides, which may be difficult to interpret because of poor contrast or poor color differentiation. The Chromatic Contrast Visualization System (CCV) combines an optical microscope with electronically controlled light-emitting diodes (LEDs) in order to generate adjustable intensities of RGB channels for sample illumination. While most image enhancement techniques rely on software post-processing of an image acquired under standard illumination conditions, CCV produces real-time variations in the color composition of the light source itself. The possibility of covering the entire RGB chromatic range, combined with the optical properties of the different tissues, allows for a substantial enhancement in image details. Traditional image acquisition methods do not exploit these visual enhancements which results in poorer visual distinction among tissue structures. Photodynamic therapy (PDT) procedures are of increasing interest in the treatment of several forms of cancer. This study uses histological slides of rat liver samples that were induced to necrosis after being exposed to PDT. Results show that visualization of tissue structures could be improved by changing colors and intensities of the microscope light source. PDT-necrosed tissue samples are better differentiated when illuminated with different color wavelengths, leading to an improved differentiation of cells in the necrosis area. Due to the potential benefits it can bring to interpretation and diagnosis, further research in this field could make CCV an attractive technique for medical applications.