Acquisition Algorithm Research Articles

Classifier-based constraint acquisition

Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) non-solutions. Active methods query an oracle while passive methods do not. We propose a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier. We discuss a wide range of possible new acquisition methods with useful properties inherited from classifiers. We also show the potential of this approach using a Naive Bayes classifier, obtaining a new passive acquisition algorithm that is considerably faster than existing methods, scalable to large constraint sets, and robust under errors.

Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

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Upscaling X-ray nanoimaging to macroscopic specimens.

Upscaling X-ray nanoimaging to macroscopic specimens has the potential for providing insights across multiple length scales, but its feasibility has long been an open question. By combining the imaging requirements and existing proof-of-principle examples in large-specimen preparation, data acquisition and reconstruction algorithms, the authors provide imaging time estimates for howX-ray nanoimaging can be scaled to macroscopic specimens. To arrive at this estimate, a phase contrast imaging model that includes plural scattering effects is used to calculate the required exposure and corresponding radiation dose. The coherent X-ray flux anticipated from upcoming diffraction-limited light sources is then considered. This imaging time estimation is in particular applied to the case of the connectomes of whole mouse brains. To image the connectome of the whole mouse brain, electron microscopy connectomics might require years, whereas optimized X-ray microscopy connectomics could reduce this to one week. Furthermore, this analysis points to challenges that need to be overcome (such as increased X-ray detector frame rate) and opportunities that advances in artificial-intelligence-based 'smart' scanning might provide. While the technical advances required are daunting, it is shown that X-ray microscopy is indeed potentially applicable to nanoimaging of millimetre- or even centimetre-size specimens.

Feasibility of Total Variation Noise Reduction Algorithm According to Various MR-Based PET Images in a Simultaneous PET/MR System: A Phantom Study.

Recently, the total variation (TV) algorithm has been used for noise reduction distribution in degraded nuclear medicine images. To acquire positron emission tomography (PET) to correct the attenuation region in the PET/magnetic resonance (MR) system, the MR Dixon pulse sequence, which is based on controlled aliasing in parallel imaging, results from higher acceleration (CAIPI; MR-ACDixon-CAIPI) and generalized autocalibrating partially parallel acquisition (GRAPPA; MR-ACDixon-GRAPPA) algorithms are used. Therefore, this study aimed to evaluate the image performance of the TV noise reduction algorithm for PET/MR images using the Jaszczak phantom by injecting 18F radioisotopes with PET/MR, which is called mMR (Siemens, Germany), compared with conventional noise-reduction techniques such as Wiener and median filters. The contrast-to-noise (CNR) and coefficient of variation (COV) were used for quantitative analysis. Based on the results, PET images with the TV algorithm were improved by approximately 7.6% for CNR and decreased by approximately 20.0% for COV compared with conventional noise-reduction techniques. In particular, the image quality for the MR-ACDixon-CAIPI PET image was better than that of the MR-ACDixon-GRAPPA PET image. In conclusion, the TV noise-reduction algorithm is efficient for improving the PET image quality in PET/MR systems.

A proposed data acquisition system and algorithm for signal processing of moving-coil geophone\u2019s output

The study of different types of vibrational and seismic movements is important for exploration in strata monitoring, machine health monitoring, earthquake detection, etc. In order to study these vibrational movements, it needs to be acquired first for analysis. Data acquisition using the seismic sensors is a challenging task. This paper presents a data acquisition system developed to acquire seismic signals from a moving-coil geophone. The paper also discusses a signal interpretation algorithm that is devised to perform automatic detection of a seismic event occurrence by separating through the waveform and non-waveform components in the sensor’s output using Gaussian naive Bayes classifier and Kernel density estimation technique. The proposed method is effective in the identification of a useful signal and identification of its nature of origin. Accuracy of the algorithm was 99% for the waveform classification. Sensitivity of the data acquisition system for the seismic sensors was 1.589 µm s–1. Further, the developed data acquisition system and the algorithm can be used in mines for seismological studies aimed at separating the vibration signal generated due to explosion and the one caused due to Earth’s tectonic and seismic activities.

Task-Oriented Deep Reinforcement Learning for Robotic Skill Acquisition and Control.

Reinforcement learning (RL) and imitation learning (IL), especially equipped with deep neural networks, have been widely studied for autonomous robotic skill acquisition and control tasks. However, these methods and their extensions require extensive environmental interactions during training, which greatly prevents them from being applied to real-world robots. To alleviate this problem, we present an efficient model-free off-policy actor-critic algorithm for robotic skill acquisition and continuous control, by fusing the task reward with a task-oriented guiding reward, which is formulated by leveraging few and imperfect expert demonstrations. In this framework, the agent can explore the environment more intentionally, thus sampling efficiency can be achieved; moreover, the agent can also exploit the experience more effectively, thereby substantially improved performance can be realized simultaneously. The empirical results on robotic locomotion tasks show that the proposed scheme can lower sample complexity by 2-10 times in contrast with the state-of-the-art baseline deep RL (DRL) algorithms, while achieving performance better than that of the expert. Furthermore, the proposed algorithm achieves significant improvement in both sampling efficiency and asymptotic performance on tasks with sparse and delayed reward, wherein those baseline DRL algorithms struggle to make progress. This takes a substantial step forward to implement these methods to acquire skills autonomously for real robots.

Ellipsoidal one-class constraint acquisition for quadratically constrained programming

We propose Ellipsoidal One-Class Constraint Acquisition (EOCCA), a fast and scalable algorithm for the acquisition of constraints for Mixed-Integer Quadratically Constrained Programming (MIQCP) models from data. EOCCA acquires a well-formed MIQCP model using solely the examples of the feasible solutions to this model. It combines x-means partitioning, standardization, and principal components analysis to preprocess the training set and then wraps the preprocessed data into several hyper-ellipsoids expressed using MIQCP constraints. These MIQCP constraints are projected back to the space of the original training set, and their further use does not require data preprocessing. Experimental evaluation shows that EOCCA scores better than a state-of-the-art algorithm in terms of fidelity of the acquired constraints to ground-truth constraints and achieves this in few orders of magnitude shorter time. We demonstrate the practical use case of EOCCA in a fully automated workflow of modeling and optimization of a rice farm using real-world data.

Lung shunt and lung dose calculation methods for radioembolization treatment planning.

Radioembolization, also known as selective internal radiation therapy (SIRT), is firmly established in the management of patients with unresectable liver cancers. Advances in normal and tumor liver dosimetry and new knowledge about tumor dose response relationships have helped promote the safe use of higher prescribed doses, consequently transitioning radioembolization from palliative to curative therapy. The lungs are considered a critical organ of risk for radioembolization treatment planning. Unfortunately, lung dosimetry has not achieved similar advances in dose calculation methodology as liver dosimetry. Current estimations of lung dose are dependent on a number of parameters associated with data acquisition and processing algorithms, leading to poor accuracy and precision. Therefore, the efficacy of curative radioembolization may be compromised in patients for whom the lung dose derived using currently available methods unnecessarily limits the desired administered activity to the liver. We present a systematic review of the various methods of determining the lung shunt fraction (LSF) and lung mean dose (LD). This review encompasses pretherapy estimations and post-therapy assessments of the LSF and LD using both 2D planar and 3D SPECT/CT based calculations. The advantages and limitations of each of these methods are deliberated with a focus on accuracy and practical considerations. We conclude the review by presenting a lexicon to precisely describe the methodology used for the estimation of LSF and LD; specifically, category, agent, modality, contour and algorithm, in order to aid in their interpretation and standardization in routine clinical practice.

Automated analytical procedure using multicommuted flow analysis and organic solvent extraction controlled by an Arduino Due board for photometric determination of zinc in water

In this study, an automated analytical procedure that utilizes solvent extraction for the photometric determination of zinc in water is described. The procedure involved the reaction of diphenylthiocarbazone (dithizone) with Zn(II) in the alkaline medium and extraction with chloroform. The instrumental setup, combined with flow-batch and multicommuted flow analyses approaches, was designed to facilitate microextraction without using a dispersant organic solvent. Fluid propulsion occurred through two mini peristaltic pumps and a homemade syringe pump. Photometric detection was performed using a homemade LED-based photometer equipped with a flow cell (50 mm in length). The control of the instrumental setup, analytical procedure, and data acquisition algorithm was achieved using an Arduino Due board along with a software developed for this study. After determining the optimal values of the control variables using Zn(II) standard solutions, the effectiveness of the proposed method was evaluated. For this purpose, a set of water samples was analyzed. For assessing accuracy, the samples were also analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES). By comparing the paired t-test results of the two analyses, it was observed that at 95% confidence level, there is no significant difference. Other advantageous results of the proposed method were as follows: the linear response range was 10–100 µgL−1 (r2 = 0.9934), the limit of detection was 8.3 µgL−1, the coefficient of variation was 3.3% (n = 9), the consumption of chloroform and dithizone was 200 µL and 4 µg, respectively, per determination, and the sampling rate was 19 determinations per hour.

Retracted] Research on Acquisition and Tracking Algorithm of Global Satellite Positioning Receiver Based on UWB

In order to overcome the problems of the traditional algorithm, such as the time‐consuming execution of acquisition instructions, low signal tracking accuracy, and low signal capture accuracy, a global satellite positioning receiver acquisition and tracking algorithm based on UWB technology is designed in this study. On the basis of expounding the pulse generation method and working principle in UWB technology, this paper analyzes in detail the characteristics of UWB technology, such as antimultipath, low power consumption, and strong penetration. Then, on the basis of window function filtering, in the process of three‐dimensional search of global satellite positioning signal, firstly, the satellite signal entering the GPS software receiver is processed by RF front‐end mixing and AD sampling, and then, the signal tracking and navigation message solving are completed according to the relationship between the influence factor and Doppler frequency offset. The experimental results show that the execution time of the acquisition instruction of the proposed algorithm varies between 1129 ms and 1617 ms; the signal tracking accuracy ranges between 0.931 and 0.951, and the signal capture accuracy ranges between 93.3% and 95.6%, which proves that the proposed algorithm has achieved the design expectation.

Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm.

Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reason, this paper proposes an improved neural network optimized by mutation ant colony algorithm for automatic acquisition of film labels, which not only overcomes the disadvantages of traditional neural network, such as difficulty in determining weights, slow convergence speed, and easiness to fall into local minimum, but also makes up for the shortcomings faced by using ant colony algorithm alone through the gradient information of quantum genetic algorithm neural network. The results show that the user similarity judgment is added in the process of calculating the user rating deviation between movies, and the neighbor chooses to add the movie tag weight and rating similarity as the basis for the neighbor selection of the target movie in the process of predicting the target movie rating. Experiments show the effectiveness of the algorithm.

Imaging Motion: A Comprehensive Review of Optical Coherence Tomography Angiography.

Optical coherence tomography (OCT) is a three-dimensional (3-D) optical imaging technology that provides noninvasive, micrometer resolution images of structural interiors within biological samples with an approximately 1~2 mm penetration depth. Over the last decades, advances in OCT have revolutionized biomedical imaging by demonstrating a potential of optical biopsy in preclinical and clinical settings. Recently, functional OCT imaging has shown a promise as angiography to visualize cell-perfused vasculatures in the tissue bed in vivo without requiring any exogenous contrast agents. This new technology termed OCT angiography (OCTA) possesses a unique imaging capability of delineating tissue morphology and blood or lymphatic vessels down to capillaries at real-time acquisition rates. For the past 10years since 2007, OCTA has been proven to be a useful tool to identify disorder or dysfunction in tissue microcirculation from both experimental animal studies and clinical studies in ophthalmology and dermatology. In this section, we overview about OCTA including a basic principle of OCTA explained with simple optical physics, and its scan protocols and post-processing algorithms for acquisition of angiography. Then, potential and challenge of OCTA for clinical settings are shown with outcomes of human studies.

DGLFV: Deep Generalized Label Algorithm for Finger-Vein Recognition

With the growing demand for information security, finger-vein recognition has become widespread. However, the robustness of the recognition process becomes a major problem. When identifying unseen categories with traditional finger-vein recognition systems, a few issues remain, such as recognition interference and low efficiency. This paper proposes a Deep Generalized Label Finger-Vein (DGLFV) model to extract feature maps and achieve high-accuracy recognition. The largest rectangular finger-vein region is extracted through image semantic segmentation and the advanced bidirectional traversing and center diffusion method for the known categories. Then we generalize all the unseen categories actively as Class C+1 to reduce interference from unregistered users. Furthermore, an adaptive threshold acquisition algorithm is proposed for Label Receiver Operating Characteristic (LROC), so that the procedures of classification, recognition, and verification are unified. Apart from Shandong University Homologous Multi-modal Traits (SDUMLA-HMT), we have conducted additional experiments on our self-built database, Finger Veins of Signal and Information Processing Laboratory (FV-SIPL). The recognition accuracy of the approach proposed in this paper has reached 99.25% and 99.08% testing on FV-SIPL and SDUMLA-HMT, with a low error rate at 1.481% and 2.228% and little time consumption of 0.157s for a single image, which is better than most state-of-the-art finger-vein recognition methods.

Research on accelerating CEE-DGCNN event extraction algorithm based on multi-CPU platform

With the development of the Internet, the volume of information is expanding rapidly, and the complex information makes it particularly important to extract information quickly and intelligently. Event extraction algorithm is such a kind of fast and accurate information acquisition algorithm. With the development of deep learning in recent years, deep learning also shines in the field of time extraction. CEE-DGCNN network is an efficient and accurate event extraction algorithm for Chinese text. At present, the acceleration research on deep learning in the academic community mainly focuses on GPU, FPGA and other platforms. In fact, compared with these platforms, CPU is more versatile. Considering that the algorithm should have universality and universality in industrial application, this paper makes an in-depth analysis of CEE-DGCNN algorithm to study its acceleration on CPU. Experiments were carried out on x86 and ARM architectures respectively, and good acceleration effects were achieved.

Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

The currently applied maintenance strategies, including Reactive and Preventive maintenance can be considered obsolete. The constant improvements in Information and Communication Technologies as well as in Digital Technologies along with the increase of computational power, have facilitated the development of new Artificial Intelligence algorithms to integrate cognition in computational systems. This trend is posing a great challenge for engineers, as such developments will enable the creation of robust systems that can monitor the current status of the machines and by extension to predict unforeseeable situations. Furthermore, Smart Computers will be capable of examining all possible scenarios and suggest viable solutions in a fraction of time compared to humans. Therefore, in this paper, the modelling, design and development of a Predictive Maintenance and Remote Monitoring system are proposed, based on the utilization of Artificial Intelligence algorithms for data acquisition, fusion, and post-processing. In addition to that, the proposed framework will integrate a Mixed Reality application for the intuitive visualization of the data, that will ultimately facilitate production and maintenance engineers to monitor the condition of the machines, and most importantly to get an accurate prediction of the oncoming failures.

Theoretical assessment and application of modern GNSS signals acquisition performance

The application of the Global Navigation Satellite System (GNSS) is extensive and has widely infiltrated the national economy and social development in various fields. As critical part of the satellite navigation system design process, GNSS signals performance assessment has received tremendous attention for a long period of time. The mathematical model of GNSS signal detection is built based on single point detection test. An improved acquisition performance assessment method that considers the modulation type, code search step-size, and acquisition algorithms is proposed herein. This study investigates the performance of the overall detection and main/side peak detection for Binary Offset Carrier (BOC), Multiplexed Binary Offset Carrier (MBOC), and Alternative BOC (AltBOC) signals using matched and unambiguous acquisition methods, and obtains quantitative results. With a fixed code search step-size, the multi-peaks of the autocorrelation functions of BOC, MBOC, and AltBOC signals will degrade the acquisition performance. The higher the sub-carrier rate, the more serious is the degradation. Although signal-power loss occurs to some extent, Binary Phase-Shift Keying-like (BPSK-like) and Sub Carrier Phase Cancelation (SCPC) methods significantly improve the acquisition performance.

Development of a method for studying the impact of the time reserve value on the reliability of the train schedule based on the epidemiological SIR model

It is important to find rational values of time reserves to compensate for delays during train movement to ensure train schedule reliability. This study proposes to investigate the impact of different categories of trains and their time reserve on schedule reliability. The main purpose of the study is to develop a method for studying the extent of the impact of time reserve on train schedule reliability based on the epidemiological model. Mathematical train delay propagation at the section model has been developed on the basis of the Susceptible – Infected – Removed (SIR) epidemiological model of adaptation type (taking into account the effect of using different train categories. It is suggested to use a binary genetic algorithm to search for parameters of delay propagation rate in the SIR model. According to a specially developed data acquisition algorithm, empirical data on the propagation of train delays at the station were obtained. Experimental studies have been carried out to search for train delay propagation velocity coefficients in the SIR model. The train movement restoration results during a delay at the station are given to study the impact of the time reserve value on train schedule reliability, taking into account the speed recovery delay set in the SIR model. The simulation results confirm the developed mathematical model adequacy.

Detection of high-frequency oscillations in electroencephalography: A scoping review and an adaptable open-source framework

PurposeHigh frequency oscillations (HFOs) are putative biomarkers of epileptogenicity. These electrophysiological phenomena can be effectively detected in electroencephalography using automated methods. Nonetheless, the implementation of these methods into clinical practice remains challenging as significant variability exists between algorithms and their characterizations of HFOs. Here, we perform a scoping review of the literature pertaining to automated HFO detection methods. In addition, we propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar parameters. MethodsSeveral databases (OVID Medline, Web of Science, PubMed) were searched for articles presenting novel, automated HFO detection methods. Details related to the algorithm and various stages of data acquisition, pre-processing, and analysis were abstracted from included studies. ResultsFrom the 261 records screened, 57 articles presented novel, automated HFO detection methods and were included in the scoping review. These algorithms were categorized into 3 groups based on their most salient features: energy thresholding, time-frequency analysis, and data mining/machine learning. Algorithms were optimized for specific datasets and suffered from low specificity. A framework for user-constrained inputs is proposed to circumvent some of the weaknesses of highly performant detectors. ConclusionsFurther efforts are required to optimize and validate existing automated HFO detection methods for clinical utility. The proposed framework may be applied to understand and standardize the variations in HFO definitions across institutions.

A Rapid Serial Timing Acquisition Algorithm for Hybrid DS/FFH Packet Radio Communication

In this article, we propose a simple, reliable, and rapid serial timing acquisition algorithm for hybrid direct-sequence and fast frequency-hopping packet radio communication systems. The proposed algorithm is designed in both the frequency domain of frequency hopping and the pseudonoise code domain of direct sequence. We provide the performance analysis for our proposed algorithm and confirm its exactness by simulation. Compared with the conventional serial search algorithm, the proposed algorithm shows much faster acquisition time while obtaining similar performance.