Acquisition Sets Research Articles

Sensor data reduction with novel local neighborhood information granularity and rough set approach

Data description and data reduction are important issues in sensors data acquisition and rough sets based models can be applied in sensors data acquisition. Data description by rough set theory relies on information granularity, approximation methods and attribute reduction. The distribution of actual data is complex and changeable. The current model lacks the ability to distinguish different data areas leading to decision-making errors. Based on the above, this paper proposes a neighborhood decision rough set based on justifiable granularity. Firstly, the rough affiliation of the data points in different cases is given separately according to the samples in the neighborhood. Secondly, the original labels are rectified using pseudo-labels obtained from the label noise data that has been found. The new judgment criteria are proposed based on justifiable granularity, and the optimal neighborhood radius is optimized by the particle swarm algorithm. Finally, attribute reduction is performed on the basis of risky decision cost. Complex data can be effectively handled by the method, as evidenced by the experimental results.

Endoscopic Tactile Capsule for Non-Polypoid Colorectal Tumour Detection

An endoscopic tactile robotic capsule, embedding miniaturized MEMS force sensors, is presented. The capsule is conceived to provide automatic palpation of non-polypoid colorectal tumours during colonoscopy, since it is characterized by high degree of dysplasia, higher invasiveness and lower detection rates with respect to polyps. A first test was performed employing a silicone phantom that embedded inclusions with variable hardness and curvature. A hardness-based classification was implemented, demonstrating detection robustness to curvature variation. By comparing a set of supervised classification algorithms, a weighted 3-nearest neighbor classifier was selected. A bias force normalization model was introduced in order to make different acquisition sets consistent. Parameters of this model were chosen through a particle swarm optimization method. Additionally, an ex-vivo test was performed to assess the capsule detection performance when magnetically-driven along a colonic tissue. Lumps were identified as voltage peaks with a prominence depending on the total magnetic force applied to the capsule. Accuracy of 94 % in hardness classification was achieved, while a 100 % accuracy is obtained for the lump detection within a tolerance of 5 mm from the central path described by the capsule. In real application scenario, we foresee our device aiding physicians to detect tumorous tissues.

Inconsistent analytic strategies reduce robustness in fear extinction via skin conductance response.

Robustness of fear conditioning and extinction paradigms has become increasingly important for many researchers interested in improving the study of anxiety and trauma disorders. We recently illustrated the wide variability in data analysis techniques in this paradigm, which we argued may result in a lack of robustness. In the current study, we resampled data from six of our own fear acquisition and extinction data sets, with skin conductance as the outcome. In the resampled and original data sets, we found that effect sizes that were calculated using discrepant statistical strategies, sourced from a non-exhaustive search of high-impact articles, were often poorly correlated. The main contributors to poor correlations were the selection of trials from different stages of each experimental phase and the use of average compared to trial-by-trial analysis. These findings reinforce the importance of focusing on robustness in the psychophysiological measurement of fear acquisition and extinction in the laboratory and may guide prospective researchers in which decisions may most impact the robustness of their results.

A Fault Diagnosis Method of Industrial Robot Rolling Bearing Based on Data Driven and Random Intuitive Fuzzy Decision

The industrial robot is a mechanized electronic device that functions as a human arm, wrist and hand. Rolling bearings are an essential part of these flexible rotation. Due to the harsh environment of the industrial robot and full-load operation, bearing faults are difficult to be diagnose and occur from time to time. In the long-term research, the author found that the traditional method based on fault characteristic frequency has at least two problems. On the one hand, some bearing parameters are not easy to obtain so that the fault characteristic frequency cannot be calculated, especially the high-precision bearing of some imported equipment. On the other hand, some bearings can calculate the fault characteristic frequency, but the fuzziness of the method is difficult to overcome. This paper introduces a new method based on experimental data-driven random fuzzy evidence acquisition and intuitionistic fuzzy sets fusion (IFSF). Firstly, this method does not need to calculate the fault characteristic frequency, by constructing and matching the fuzzy expert system and the sample to be tested. The maximum value of the vertical coordinate of the intersection point, namely the likelihood measure value, is used as the membership degree of the support proposition. Then, the essential meaning of uncertainty parameter is analyzed, the membership degree of the fuzzy set under the random set framework is transformed into the membership function of the intuitionistic fuzzy set, and the binary likelihood pair is used to represent the single likelihood measure value. Finally, the single sensor multi-feature fusion and multi-sensor information fusion are transformed into intuitionistic fuzzy set multi-attribute decision fusion. The experimental results show that the method proposed in this paper can overcome the fuzziness of the traditional method, and provides a new theoretical method for the fault diagnosis of rolling bearing, which is difficult to obtain geometric parameters.

Continuum removal for ground-based LWIR hyperspectral infrared imagery applying non-negative matrix factorization.

Continuum removal is vital in hyperspectral image analysis. It enables data to be used for any application and usually requires approximations or assumptions to be made. One of these approximations is related to the calculation of the spectra of the background's blackbody temperature. Here, we present a new method to calculate the continuum removal process. The proposed method eliminates the calculation for ground-based hyperspectral infrared imagery by applying two acquisition sets before and after using the heating source. The approach involves a laboratory experiment on a long-wave infrared (LWIR; 7.7-11.8μm), with a LWIR-macro lens, an Infragold plate, and a heating source. To calculate the continuum removal process, the approach applies non-negative matrix factorization (NMF) to extract Rank-1 NMF, estimate the downwelling radiance, and compare it with that of other conventional methods. NMF uses gradient-descent-based rules (GD) and non-negative least-squares (NNLS) optimization algorithms to obtain Rank-1 NMF. A comparative analysis is performed with 1%-20% additive noise for all algorithms by using the spectral angle mapper and normalized cross correlation (NCC). Results reveal the promising performance of NMF-GD (average of 72.5% similarity percentage using NCC) and NMF-NNLS (average of 77.6% similarity percentage using NCC).

Thin-slice Free-breathing Pseudo-golden-angle Radial Stack-of-stars with Gating and Tracking T1-weighted Acquisition: An Efficient Gadoxetic Acid-enhanced Hepatobiliary-phase Imaging Alternative for Patients with Unstable Breath Holding.

Purpose:To compare four free-breathing scan techniques for gadoxetic acid-enhanced hepatobiliary phase imaging with conventional breath-hold scans.Materials and Methods:Gadoxetic acid-enhanced hepatobiliary phase imaging with six image acquisition sets performed in 50 patients. Image acquisition sets included fat-suppressed 3D T1-weighted turbo field echo with free-breathing pseudo-golden-angle radial stack-of-stars (FBRS) acquisition, FBRS with track (FBRST), FBRS with gate and track (FBRSG&T), thin-slice FBRS with gate and track (thin-slice FBRSG&T), free-breathing Cartesian acquisition (CartesianFB), and breath-hold Cartesian acquisition (CartesianBH). Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and image quality compared to the six-image acquisition sets.Results:Signal-to-noise ratio and CNR were significantly higher in FBRS, FBRST, FBRSG&T, and thin-slice FBRSG&T than in CartesianFB and CartesianBH (P < 0.001). Based on sharpness, motion artifacts, visibility of intrahepatic vessels, and overall image quality, thin-slice FBRSG&T had the highest image quality followed by CartesianBH and FBRSG&T (P < 0.001). Severe motion artifacts were observed in 25 patients in CartesianFB and three patients in CartesianBH, whereas image quality remained above the acceptable range in FBRSG&T, FBRST, FBRS, and thin-slice FBRSG&T in all cases.Conclusion:Thin-slice FBRSG&T demonstrated excellent image quality compared with conventional CartesianBH in gadoxetic acid-enhanced hepatobiliary phase imaging. It can be apply to supplemental sequences of patients with unstable breath holding.

Face recognition outside the visible spectrum

Automated face recognition (FR) is a well-studied problem, with a history of more than three decades. Facial recognition software that uses two dimensional (2D) images has advanced significantly in terms of accuracy over recent years. Despite the steps forward, face recognition is still not considered to be a solved problem for the cases of (i) difficult exposure conditions, such as during night-time, or in environments with unconstrained lighting, (ii) when operating at long standoff distances or at variable distances from the camera, and (iii) when using different camera sensors that operate in the same or different spectral bands. In practical forensic scenarios, it is often the case that investigators need to operate in difficult conditions, where face images captured in the aforementioned challenging cases need to be matched against good quality face images (gallery set) and where, grouping of the data in the context of demographic information (in terms of gender, ethnicity or other soft biometrics) may also be used in order to assist law enforcement officials, forensic investigators and security personnel in subject identification. In fact, in such practical scenarios, human recognition based solely on visible spectral images may not be feasible. Hence, an attractive solution is to develop efficient FR systems that can work in different practical scenarios. While different systems have been discussed and proposed over the years, an interesting approach is to increase the plurality of the source data, or in other words, start using data captured by camera sensors that cover a broad range of the electromagnetic spectrum that falls outside the visible band, including the ultraviolet (UV) and the infrared (IR) band. Thus, FR outside the visible spectrum is considered an area of growing research interest. This is further supported by the burgeoning market of camera sensors that continues to open new opportunities, at different spectral and spatial scales, in a large number of applications including those in security, forensics and defense. In light of these thoughts, the focus of this work is to expose readers to a plethora of FR advancements when operating outside the visible spectrum and our perspective on where this field is going. The discussion will include the latest studies in the UV (100–400nm), active IR band (0.7–2.5μm) and, finally, the passive IR band, or more specifically, the mid-wave IR (3–5μm) and long-wave IR (8–14μm) bands. The discussion will also include research work that involves same-spectral and cross-spectral matching scenarios, where, first, face images (probes) are acquired under controlled or difficult conditions as explained above, including dealing with uncooperative subjects, and adverse environments. Then, depending on the FR scenario, the acquired probe images are matched against mug shots collected in a controlled indoor environment using a conventional camera. This paper will start by briefly covering different multi-spectral FR systems and, then, discussing various related topics (ranging from data acquisition sets, up to system performance). The paper will also suggest needed new avenues of inquiry in the context of current work in multi-spectral FR system.

Optimal angular dose distribution to acquire 3D and extra 2D images for digital breast tomosynthesis (DBT)

The purpose of this study is to determine the optimal non-uniform angular dose distribution to improve the quality of the 3D reconstructed images and to acquire extra 2D projection images. In this analysis, 7 acquisition sets were generated by using four different values for the number of projections (11, 15, 21, and 29) and total angular range (±14°, ±17.5°, ±21°, and ±24.5° ). For all acquisition sets, the zero-degree projection was used as the 2D image that was close to that of standard conventional mammography (CM). Exposures used were 50, 100, 150, and 200 mR for the zero-degree projection, and the remaining dose was distributed over the remaining projection angles. To quantitatively evaluate image quality, we computed the CNR (contrast-to-noise ratio) and the ASF (artifact spread function) for the same radiation dose. The results indicate that, for microcalcifications, acquisition sets with approximately 4 times higher exposure on the zero-degree projection than the average exposure for the remaining projection angles yielded higher CNR values and were 3% higher than the uniform distribution. However, very high dose concentrations toward the zero-degree projection may reduce the quality of the reconstructed images due to increasing noise in the peripheral views. The zero-degree projection of the non-uniform dose distribution offers a 2D image similar to that of standard CM, but with a significantly lower radiation dose. Therefore, we need to evaluate the diagnostic potential of extra 2D projection image when diagnose breast cancer by using 3D images with non-uniform angular dose distributions.

Non‐convex compressed sensing with frequency mask for seismic data reconstruction and denoising

ABSTRACTCompressed Sensing has recently proved itself as a successful tool to help address the challenges of acquisition and processing seismic data sets. Compressed sensing shows that the information contained in sparse signals can be recovered accurately from a small number of linear measurements using a sparsity‐promoting regularization. This paper investigates two aspects of compressed sensing in seismic exploration: (i) using a general non‐convex regularizer instead of the conventional one‐norm minimization for sparsity promotion and (ii) using a frequency mask to additionally subsample the acquired traces in the frequency‐space () domain. The proposed non‐convex regularizer has better sparse recovery performance compared with one‐norm minimization and the additional frequency mask allows us to incorporate a priori information about the events contained in the wavefields into the reconstruction. For example, (i) seismic data are band‐limited; therefore one can use only a partial set of frequency coefficients in the range of reflections band, where the signal‐to‐noise ratio is high and spatial aliasing is low, to reconstruct the original wavefield, and (ii) low‐frequency characteristics of the coherent ground rolls allow direct elimination of them during reconstruction by disregarding the corresponding frequency coefficients (usually bellow 10 Hz) via a frequency mask. The results of this paper show that some challenges of reconstruction and denoising in seismic exploration can be addressed under a unified formulation. It is illustrated numerically that the compressed sensing performance for seismic data interpolation is improved significantly when an additional coherent subsampling is performed in the domain compared with the domain case. Numerical experiments from both simulated and real field data are included to illustrate the effectiveness of the presented method.

Dempster–Shafer Fusion of Multiple Sparse Representation and Statistical Property for SAR Target Configuration Recognition

Due to the characteristic of the synthetic aperture radar (SAR) image's sensitivity to the target aspect angles, a multiple sparse representation (MSR) method for SAR target configuration recognition is proposed. Making use of the prior information, dictionaries are constructed by using the samples of each configuration to better capture the detail information of the SAR images. The advantage of MSR over sparse representation for detail feature extraction is analyzed. Moreover, to achieve better recognition results, the Dempster–Shafer fusion is carried out to get comprehensive description of the target for configuration recognition. Two mass functions are constructed based on MSR and the sample statistical property. The combined mass function has the advantages of both the detail and global features of the target. Experiments on the moving and stationary target acquisition and recognition data sets validate the effectiveness and superiority of the proposed algorithm.

Optimization of the key imaging parameters for detection of microcalcifications in a newly developed digital breast tomosynthesis system

PurposeThe purpose of this study was to investigate the effect of different acquisition parameters and to characterize their relationships in order to improve the detection of microcalcifications using digital breast tomosynthesis (DBT). Materials and methodsDBT imaging parameters were optimized using 32 different acquisition sets with 6 angular ranges (±5°, ±10°, ±13°, ±17°, ±21°, and ±25°) and 8 projection views (PVs) (5, 11, 15, 21, 25, 31, 41, and 51 projections). To investigate the effects of variable angular dose distribution, the acquisition sets were evaluated with delivering more dose toward the central views. ResultsOur results show that a wide angular range improved the reconstructed image quality in the z-direction. If a large number of projections are acquired, then electronic noise may dominate the contrast-to-noise ratio (CNR) due to reduced radiation dose per projection. With delivering more dose toward the central views, it was found that the vertical resolution was reduced with increasing dose in the central PVs. On the other hand, the CNR clearly increased with increasing concentration of dose distribution in central views. ConclusionsAlthough it was found that increasing angular range improved the vertical resolution, it was also found that the image quality of microcalcifications in the in-focus plane did not improve by increasing the noise due to greater effective breast thickness. Angular dose distributions, with more dose delivered to the central views, generally yielded a higher quality factor than uniform dose distributions.

Effect of acquisition parameters on digital breast tomosynthesis: Total angular range and number of projection views

The purpose of this study was to investigate the effect of different acquisition parameters and to determine the optimal set of acquisition parameters of projection views (PVs) for the new developed digital breast tomosynthesis (DBT) system. The DBT imaging parameters were optimized using 32 different acquisition sets with six angular ranges (±5°, ±10°, ±13°, ±17°, ±21°, and ±25°) and eight projection views (5, 11, 15, 21, 25, 31, 41, and 51 prjections). In addition to the contrastto-noise ratio (CNR), the artifact spread function (ASF) was used to quantify the in-focus plane artifacts along the z-direction in order to explore the relationship between the acquisition parameters and the image quality. A commercially, available breast-mimicking phantom was imaged to qualitatively verify our results. Our results show that a wide angular range improved the reconstructed image quality in the z-direction. If a large number of projections are acquired, then the electronic noise may dominate the CNR due to reduce the radiation dose per projection. Although increasing angular range was found to improve the vertical resolution, due to greater effective breast thickness, the image quality of microcalcifications in the in-focus plane was also found not to be improved by increasing the noise. Therefore, potential trade-offs of these physical imaging properties must be considered to optimize the acquisition configuration of a DBT system. Our results suggest possible directions for further improvements in DBT systems for high quality imaging.

Image quality evaluation of breast tomosynthesis with synchrotron radiation

This study investigates the image quality of tomosynthesis slices obtained from several acquisition sets with synchrotron radiation using a breast phantom incorporating details that mimic various breast lesions, in a heterogeneous background. A complex Breast phantom (MAMMAX) with a heterogeneous background and thickness that corresponds to 4.5 cm compressed breast with an average composition of 50% adipose and 50% glandular tissue was assembled using two commercial phantoms. Projection images using acquisition arcs of 24°, 32°, 40°, 48°, and 56° at incident energy of 17 keV were obtained from the phantom with the synchrotron radiation for medical physics beamline at ELETTRA Synchrotron Light Laboratory. The total mean glandular dose was set equal to 2.5 mGy. Tomograms were reconstructed with simple multiple projection algorithm (MPA) and filtered MPA. In the latter case, a median filter, a sinc filter, and a combination of those two filters were applied on the experimental data prior to MPA reconstruction. Visual inspection, contrast to noise ratio, contrast, and artifact spread function were the figures of merit used in the evaluation of the visualisation and detection of low- and high-contrast breast features, as a function of the reconstruction algorithm and acquisition arc. To study the benefits of using monochromatic beams, single projection images at incident energies ranging from 14 to 27 keV were acquired with the same phantom and weighted to synthesize polychromatic images at a typical incident x-ray spectrum with W target. Filters were optimised to reconstruct features with different attenuation characteristics and dimensions. In the case of 6 mm low-contrast details, improved visual appearance as well as higher contrast to noise ratio and contrast values were observed for the two filtered MPA algorithms that exploit the sinc filter. These features are better visualized at extended arc length, as the acquisition arc of 56° with 15 projection images demonstrates the highest image reconstruction quality. For microcalcifications, filtered MPA implemented with a combination of median and sinc filters indicates better feature appearance due to efficient suppression of background tissue. The image quality of these features is less sensitive to the acquisition arc. Calcifications with size ranging from 170 to 500 μm, like the ones presently studied, are well identified and visualized for all arcs used. The comparison of single projection images obtained under different beam conditions showed that the use of monochromatic beam can produce an image with higher contrast and contrast to noise ratio compared to an image corresponding to a polychromatic beam even when the latter is acquired with double incident exposure. Filter optimization in respect to the type of feature characteristics is important before the reconstruction. The MPA combined with median and sinc filters results in improved reconstruction of microcalcifications and low-contrast features. The latter are better visualized at extended arc length, while microcalcifications are less sensitive to this acquisition parameter. Use of monochromatic beams may result in tomographic images with higher contrast acquired at lower incident exposures.

Prediction of the chaotic time series using neuro-fuzzy networks

A novel hybrid neural fuzzy inference system is presented. Only based on the desired input-output data pairs, are the knowledge acquisition and initial fuzzy rule sets available. Then, employing neural networks learning techniques, the fuzzy logic rules,input-output fuzzy membership functions and weights in networks can be easily tuned. So the rule matching is reduced ,inferencing is accelerated, adaptability of the system is greatly improved. To illustrate the performance of the proposed neuro-fuzzy hybrid model, simulations on the chaotic Mackey-Glass time series prediction are performed. Combining either off-line or on-line learning with the proposed hybrid model, we can show that the chaotic Mackey-Glass time series are accurately predicted, and demonstrate the effectivness of the model.

ELECTRICAL GROUND SUPPORT EQUIPMENT (EGSE) DESIGN FOR SMALL SATELLITE

This paper describes EGSE design for the small satellite such like KOMPSAT-2 satellite. Recent design trend of small satellite and EGSE is to take short development time and less cost. For this purpose, the design for KOMPSAT-2 satellite and EGSE are not much modified from KOMPSAT-1 heritage. It means that it is able to be accommodated the verified hardware and software modules used in KOMPSAT-1 satellite program if possible. The objective of EGSE is to provide hardware and software for efficient electrical testing of integrated KOMPSAT-2 satellite in three general categories. (1) Simulators for ground testing (e.g. solar-simulation power, earth scenes, horizons and sun sensor). (2) Ground station type satellite data acquisition and processing test sets. (3) Overall control of satellite using hardline datum. In KOMPSAT (KOrea Multi-Purpose SATellite) program, KOMPSAT-2 EGSE was developed to support satellite integration and test activities. The KOMPSAT-2 EGSE was designed in parallel with satellite design. Consequently, the KOMPSAT-2 EGSE was based on the KOMPSAT-1 heritage since the spacecraft design followed the heritage. The KOMPSAT-2 baseline was elaborated by taking advantage of experience from KOMPSAT-1 program. The EGSE of KOMPSAT-2 design concept is generic modular design with preference in part selection with commercial off-the-shelf which were proven from KOMPSAT-1 programs, flexible/user friendly operational environment (graphical interface preferred), minimized new design and self test capability.

High-resolution three-dimensional imaging from subsecond helical CT data sets: applications in vascular imaging.

T he coupling of three-dimensional (3D) imaging with helical CT data sets has shown promise in a wide range of clinical applications in the chest, abdomen, CNS, and vascular systems [ 1 , 2]. The potential ability to create vascular maps is a particularly exciting area, with initial results suggesting that for specific applications, 3D CT could replace conventional angiography [1-3] at a considerable savings in both cost and potential morbidity. Numerous articles have attempted to address the importance of data acquisition protocols with a focus on the role of slice thickness and pitch and the inevitable compromises in parameter selection [2-4]. Other articles have focused on the technical aspects of the 3D reconstruction process, with the advantages and disadvantages of shaded surface display, maximum intensity projection, and volume-rendering technique debated in the literature [4]. One of the most recent advances has been the subsecond scan time capability of helical CT scanners. These scan times, which are in the range of 0.75 sec. do not reach the 100msec acquisitions of the so-called ‘fast scanners” (Electron Beam Scanner; Imatron, Oyster Bay, CA). However, they do provide the capability of even fasten slice acquisition and better volume data sets. Herts et al. [5] compared image quality of helical CT scans in which times of acquisition were 0.75 sec and 1 sec. These authors found little perceived difference for measures of organ edge sharpness, vessel enhancement, visibility, and image quality. In this article, we illustrate some of the vascular detail that is possible with these subsecond techniques and the potential role they can play in clinical diagnosis and patient care.