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 Subjects -> ELECTRONICS (Total: 184 journals)
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 Sensing and Imaging : An International JournalJournal Prestige (SJR): 0.255 Citation Impact (citeScore): 1Number of Followers: 2      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1557-2072 - ISSN (Online) 1557-2064 Published by Springer-Verlag  [2351 journals]
• An Automatic Stopping Criterion for Contrast Enhancement Using Multi-scale
Top-Hat Transformation
• Abstract: Abstract Image contrast enhancement is frequently referred to as one of the most important issues in image processing because it is a necessary pre-processing step in many computer vision and image processing algorithms. Contrast enhancement is normally required to increase the quality of low contrast images by expanding the dynamic range of input gray level. However, image contrast enhancement without disturbing other parameters of the image is one of the difficult tasks in image processing. To efficiently enhance images, algorithms based on multi-scale top-hat morphological transform (MSTH) have been proposed. However, scale selection to stop the algorithm is very subjective and empirical. In order to automatically select the iterations number required by MSTH algorithm, an automatic stopping criterion based on the contrast improvement ratio revisited is proposed in this paper.
PubDate: 2019-06-10

• Experimental Research of In Vivo Mouse Cardiac 4D Micro-CT Imaging via
Deformation Vector Field Registration
• Abstract: Abstract In the radiation therapy, high-quality four-dimensional (4D) Cone-beam Computed Tomography data of patients with lung cancer can be obtained by deforming prior images using estimated deformation vector fields (DVF). In pre-clinical field, meanwhile, four-dimensional Micro-Computed Tomography (4D Micro-CT) imaging is also a promising tool to explore long-term cardiopulmonary diseases via in vivo mouse models. However, since the respiration and heart rates of mice are 8 times faster than those of humans, and the higher resolution of Micro-CT even requires more projection views, the 4D Micro-CT imaging of mice is more challenging than that of human being. In this paper, we integrate the DVF registration method with retrospective gating technology, and try to realize cardiac 4D Micro-CT imaging of in vivo mouse. The whole experimental research is completed on our self-developed Micro-CT platform, and the procedures of implementation are as follows. Firstly, the multi-turn projection data through a specific scanning protocol is obtained, and then the projection views locating in the respiratory resting period are picked and categorized into different groups in terms of phase information acquired from the respiratory and ECG gating sub-system. Secondly, the selected projection data is reconstructed via the ordered subsets simultaneous algebraic reconstruction technique algorithm as prior images. Finally, the images within different cardiac phases are made by deforming the prior images using optimized DVF. The results show every one 4D Micro-CT image series with 5 cardiac phases can be secured through one scan, which all bear clear heart contours and are with less streak artifacts, and the images made by the proposed method have higher signal noise ratio compared with those by conventional algorithms. A similar performance can be obtained when the angular sampling interval decreases. Hence, the proposed 4D cardiac imaging technology can effectively characterize the cardiac motion, and can be developed as a professional tool to image the heart of a living mouse dynamically.
PubDate: 2019-06-05

• Synthetic Aperture Imaging Beyond Foreground Using Image Matting
• Abstract: Abstract Synthetic aperture imaging is widely applied to reconstruct the occluded objects. Unfortunately, due to the superimposition of foreground rays, blurriness occurs in reconstructed images, which leads to an undesirable quality of reconstruction result. Screening effective rays before synthetic aperture imaging become the key to improve the quality of reconstructed image. In this paper, we propose a method to remove interference from occlusion in synthetic aperture imaging by using image matting. By focusing on the occlusion plane, the focusing degree of synthetic aperture imaging result can roughly label occlusions and non-occlusions. These labels can serve as the scribbles of input image of image matting, which can distinguish occlusions and backgrounds more precisely. After that, the focusing at the desired depth is by averaging pixels without occlusions. The experimental results show that our method can effectively remove the blur in the reconstruction results. We demonstrate the superiority of our method by presenting experimental results as well as comparing our method with other’s methods.
PubDate: 2019-05-30

• Electrostatic Read Out for Label-Free Assays Based on Kelvin Force
Principle
• Abstract: Detection methods and analytical devices have drawn increasing attention in recent years due to their direct impact on early detection, monitoring and diagnosis of disease in medical research. In this work, we describe a simple but so far unrecognized label-free method for surface-bound analyte detection, which could be applicable to a wide range of substances. In this respect, the feasibility and practical aspects of a micrometer-scale, poly-l-lysine-based, solid-phase assay for label-free analyte detection with electrostatic read-out are investigated. Micropatterned poly-l-lysine layers were produced using soft-lithography on mica and their electrostatic surface potential was determined using Kelvin-probe Force Microscopy. Ribose, a natural sugar, was used as analyte. Upon exposure to ribose, the surface potential changed from positive to negative in a reversible manner. We report for the first time the use of an electrostatic principle for assay read-out. This purely physical effect could be used to develop label- and marker-free assays for sugars, various other substances or, possibly, biosensors. Graphical
PubDate: 2019-05-29

• Correction to: CMOS Image Sensor with Tunable Conversion Gain for Improved
Performance
• Abstract: The original version of this article unfortunately contained a mistake. Figure 1 was partly missing at the bottom in the pdf and xml versions of the article.
PubDate: 2019-05-27

• Discriminative Analysis of Depression Patients Studied with Structural MR
Images Using Support Vector Machine and Recursive Feature Elimination
• Abstract: Abstract Currently, the diagnosis of depression is largely based on clinical judgments due to the absence of objective biomarkers. There are increasing evidences that depression (DP) is associated with structural abnormalities. However, the previous analyses have a poor predictive power for individuals. To discriminate DP patients from normal controls (NCs) studied with structural magnetic resonance images using the method of support vector machine (SVM) combined with recursive feature elimination (RFE). In this study, 40 DP patients and 40 age- and sex-matched NCs were recruited from Guangzhou Brain Hospital and the local community, respectively. We calculated gray matter volume (GMV) and white matter volume (WMV) of 210 cortical and 36 subcortical regions, defined by the Human Brainnetome Atlas. The group differences between DP patients and NCs were compared. The method of SVM combined with RFE was applied into the discriminative analysis of DP patients from NCs, in which discriminative features were drawn from GMV and WMV. We found that the DP patients showed significant GMV reductions in eight brain regions and showed significant WMV reductions in ten brain regions. The classifier using GMV as input features achieved the best performance (an accuracy of 86.25%, a sensitivity of 85%, and a specificity of 87.5%) in the discriminative analyses between DP patients and NCs. These findings provided evidences that specific structural brain regions associated with DP patients might qualify as a potential biomarker for disease diagnosis, and the machine-learning method of SVM with RFE may reveal neurobiological mechanisms in distinguishing DP patients from NCs.
PubDate: 2019-05-27

• A Simple MCU and RF-Based Wetness Detector
• Abstract: Abstract Diapers rash are harmful to the babies and the elderly; so, wetness detection technologies have been sophisticated by many research worker to assist in discovering these events. The study and survey offered here looked into a radio frequency-based microcontroller and GSM for wetness detection. One of the crucial concerns is not only the detection of humidity but the Health complications of diapers rash. This study utilizes a wetness detection system with different types of alarms: audio buzz, flashing light emitting diode, and Smartphone. Our proposed system consists of a wetness sensor, microcontroller, radio frequency transceiver microcontroller, and GSM modem, or computer. Two thin wires between diaper sheets or layers of medical dressing set up the wetness sensor. The two or four thin wires connect to a couple of pressing studs sticking out from one end of the diaper or gauze. When the wires are wetted, the resistance between the studs falls down a pre-established threshold, launching the alarm. Radio Frequency transceiver passes the warning alarm through Microcontroller to the GSM modem. Consequently, Smartphone and other devices forward the alarm to whom it may concern wetness or bleeding is occurring.
PubDate: 2019-05-23

• A 3D Normal Human Ear Atlas of Voxel-Based CT Images
• Abstract: Abstract For the quantitative analysis of medical images in clinical research, diagnosis and treatment, a reliable basic framework for developing a normal human ear atlas of voxel-based computed tomography (CT) images was proposed. We annotated 10 precise ear structures with different labels from 64 patients with normal ear structures. Paired-samples t test, Pearson’s test and descriptive statistics were carried on the volume and coordinate data, which were first obtained from annotation to verify the correlation and difference. In addition, we constructed a three dimensional (3D) model of the standard human ear atlas with six views for presentation. Through a series of statistical analyses, a standard 3D normal human ear atlas containing volume and spatial data was obtained from voxel-based CT images. There was a significant negative correlation exists between age and the volume of the incus, and no correlation with other structures. There was no significant correlation between slice thickness and the volume of 10 structures. The volume of most structures on both sides is significantly correlated and there was no significant difference in the volume of most structures on both sides except for the jugular foramen. Besides, the coordinate range of the bilateral structures is relatively consistent. The specific volume and spatial data for the human ear atlas are helpful in the diagnosis of abnormalities, and this 3D normal human ear atlas will provide new insights for radiologists in clinical research.
PubDate: 2019-05-13

• Image Super-Resolution Reconstruction: A Granular Computing Approach from
the Viewpoint of Cognitive Psychology
• Abstract: Abstract Image super-resolution reconstruction is the current research focus and important field of digital image processing, and widely applied in public security, medical diagnosis, etc. Inspired by the idea of granular computing, we explore the granular computing models and algorithms of image super-resolution reconstruction integrating theories and methods of computer science, mathematics, and cognitive psychology. The research includes the following aspects. (1) The granulation method of image is proposed to transform the image space into granularity space. (2) The join operator and the meet operator between two granules are designed for the fuzzy inclusion measure mu and sigma between two granules, are used to realize the transformation between two granularity spaces with different granularities, to obtain the prior knowledge to guide the design of image super-resolution reconstruction algorithms. (3) According to up–down and bottom-up computing models, the granular computing algorithms of image super-resolution reconstruction are designed to realize the transformation from granularity space to image space in terms of the prior knowledge. Research can be summarized for learning process and reconstruction process, the learning process obtains the prior knowledge of image by granulation and forms granular computing model, and the reconstruction process reconstructs the high-resolution image of the given low-resolution image by granulation and the obtained prior knowledge. The feasibility of the preliminary study was verified by experiments. The research aims to build image super-resolution reconstruction models and algorithms satisfying human cognition.
PubDate: 2019-05-11

• Broadside Coupled Split Ring Resonator as a Sensitive Tunable Sensor for
Efficient Detection of Mechanical Vibrations
• Abstract: Abstract This paper explores the possibility of the precise determination of mechanical vibrations using metamaterial split ring resonator (SRR) structure. The amplitude of the interacting electromagnetic wave in the range of GHz frequency is directly varied in accordance with the amplitude of mechanical vibration, using a Broadside Coupled SRR (BCSRR) acting as a vibration sensor. Dependence of the spacing between the two rings on the resonance frequency of the BCSRR is used for the detection of vibration and it is achieved by allowing the spacing to change in accordance with the amplitude of mechanical vibration. For the effective sensing of mechanical vibration, the electromagnetic wave frequency is chosen at the center of the linear portion of the rising or the falling slope of the resonant curve of the BCSRR. By properly choosing the parameters of the BCSRR along with the effective tuning of the operating frequency, it is possible to detect even very weak vibrations. The chances of various distortions in the detected vibration waveform in connection with selection of the operating frequency and intensity of vibrations are also analyzed. The qualitative formulation of the detection process along with its experimental verification is presented. This novel method may find applications in the detection of mechanical vibrations caused by various man made and natural sources and may find manifold possibilities in the field of communication and instrumentation.
PubDate: 2019-05-09

• CMOS Image Sensor with Tunable Conversion Gain for Improved Performance
• Abstract: Abstract This paper describes the use of shallow trench isolation as a modified trench capacitor in CMOS image sensor. MTC helps to tune the conversion gain at the floating diffusion (FD) node. The use of MTC in pixel improves the sensitivity of the pixel without affecting the dynamic range (DR) of the pixel. The proposed pixel structure uses an existing in-built isolation trench as a capacitor. During low illumination, the MTC is separated from the floating diffusion (FD) node thus makes it easier to detect the low light signals owing to the lower FD node capacitance. However, in the event of high illumination, MTC is connected in parallel to the FD node increasing the capacity to store more charges from the photodiode. This attributed to the improved DR performance of the pixel. Using 2D numerical simulations, we show that an overall 60 dB enhancement in the DR as compared to the conventional pixel. Moreover, the proposed modification does not affect the other pixel characteristics such as dark current and crosstalk.
PubDate: 2019-04-30

• Neural Network Technique for Electronic Nose Based on High Sensitivity
Sensors Array
• Abstract: Abstract Electronic Nose, as an artificial olfaction system, has potential applications in environmental monitoring because of its proven ability to recognize and discriminate between a variety of different gases and odors. In this paper, we used a chemical sensor array to develop an electronic nose to detect and identify seven different gases (H2, C2H2, CH4, CH3OCH3, CO, NO2, and NH3). These gas sensors are chosen because of its hierarchical/doped nanostructure characteristics, which give them a very high sensitivity and low response time; we improve the linearity response and temperature dependence using models based on artificial neural networks. We used in Electronic nose a pattern recognition based on artificial neural network, which discriminates qualitatively and quantitatively seven gases and has a fast response.
PubDate: 2019-04-22

• Extended Super Resolution of Hyperspectral Images via Non-negative Sparse
Coding
• Abstract: Abstract Often high resolution (HR) RGB images generated by sparse sampling of the visible spectrum fail to produce differentiable modality for computer vision tasks, Hence computer vision tasks have to rely on gross structures in an image like corners, edges etc. instead of just the recorded reflectance of objects or materials at each pixel in a scene. In contrast to RGB, hyperspectral imaging allows pixels to record reflectance of the scene over multiple contiguous bands, which results in rich differentiable modalities. However, hyperspectral imaging, despite having a growing number of applications from agriculture, surveillance, mineralogy, food processing to eye care, is hitherto restricted to low spatial resolution imaging due to sensor hardware limitations. In this paper, we propose a hyperspectral super resolution technique to produce a high resolution (HR) hyperspectral image with a spectral support of 400–1020 nm from a low resolution (LR) hyperspectral image of the same spectral support and a high resolution multispectral (RGB) image with reduced spectral support of 400–700 nm. In the first step, we generate a HR hyperspectral prior by estimating HR hyperspectral band images in the spectral support of 400–700 nm by detail transfer and alternating iterative minimization. In the next step, we use the generated HR prior to further estimate the HR hyperspectral images for 710–1020 nm bands by learning a non-negative dictionary of reflectance spectra signatures of all the materials present in the scene from the LR hyperspectral image with spectral support in 400–1020 nm. With the estimated HR hyperspectral prior and learned dictionary, we predict the non-negative sparse codes for HR hyperspectral band images in the band of 710–1020 nm.
PubDate: 2019-04-17

• Quality Evaluation of RGB Images Reconstructed by Means of Photoacoustic
Signals
• Abstract: Abstract Recent researches have demonstrated the usefulness of photoacoustics in non-destructive control, in particular, in the monitoring and diagnosis of works of art. Indeed, it is fundamental to preserve the artworks’ integrity by using techniques not involving direct contact or damaging radiation, or pre-treatments. On the other hand, a lot of artistic heritage consists of paintings that are complex systems, where, often, the presence of highly scattering and semi-opaque materials make useless optical techniques. Consequently, in this context photoacoustics represent a powerful tool. This work is aimed to evaluate the quality of reconstructed RGB images of simple test objects examined by means of photoacoustic signals, in order to confirm the potentiality of this promising investigation method. Only a single-wavelength excitation source at 1064 nm was available and so, it has been necessary to perform some preliminary processings on the sample color images. The original images have been decomposed in R, G and B components; each of them has been converted into grayscale code, printed on transparency film and then investigated through photoacoustics. After that, the three generated photoacoustic images have been recombined to produce the reconstructed RGB image. A complete experimental system has been set to analyse dedicated test objects. The resulting images have been compared to the original ones, by using standard image quality parameters. Similar results are expected to be obtained by using three sources of distinct wavelengths (Red, Green, Blue), making the method easier to apply.
PubDate: 2019-04-15

• Projection Data Smoothing for Low-Dose CT Based on $$\ell _p$$ ℓ p
Regularization
• Abstract: Abstract Projection data smoothing is a traditional technique for low-dose computed tomography. The projection data can be modeled as a piecewise smooth function. It’s well known that $$\ell _1$$ regularization of the image gradients tries to recover piecewise constant functions, while $$\ell _2$$ regularization recovers smooth functions. This motivates us to propose the $$\ell _p$$ regularization with $$1<p<2$$ for low-dose projection data smoothing. Besides, the non-stationary Gaussian noise model for the projection data is built into the regularization term. The resulting model is then linearized such that the fast split-Bregman algorithm can be applied. Experiments on simulated projection data as well as real data show that $$\ell _p$$ regularization with $$1<p<2$$ could achieve better reconstruction compared to $$\ell _{1}$$ regularization.
PubDate: 2019-04-11

• An Approach for Increasing Sensitivity of a Tunable Micro Electro
Mechanical Sensor Using Electrostatic Hopping Voltage
• Abstract: Abstract The impetus of the study is to present a novel micro electro mechanical system based tunable gyroscope with the possibility of sensitivity enhancement using appropriate electrostatic hopping voltages. The proposed model is a silicon-based clamped–clamped micro beam sandwiched with two piezoelectric layers throughout the entire length. The nonlinear electrostatic forces are applied to the micro beam along its sense and drive mode directions (either lateral sides). The drive mode actuation is a combination of a direct current (DC) and an alternating current voltage; whereas the sense mode actuation is a pure DC voltage. The micro beam oscillates along the drive mode due to the harmonic drive mode excitation; as the micro beam undergoes base rotation, the Coriolis force induces another motion in the direction of the sense mode which is perpendicular to the drive mode direction. The more is the amplitude of the base rotation, the more is the sense mode amplitude. The sense mode amplitude is directly attributed to the magnitude of base rotation. The piezoelectric layers are actuated by a DC voltage which leads to an axial force proportional to the applied DC voltage. Exciting the piezoelectric layers changes the overall stiffness of the micro beam and as a result the operating frequency of the gyroscope becomes tunable. The partial differential equation of the motion is derived using Hamiltonian principle and discretized into two nonlinear ordinary differential equations along the drive and sense mode directions. The shooting method is used to capture the periodic motion orbits and accordingly the frequency response curves. By using Floquet theory the stability of the periodic orbits is determined. Due to the nonlinearity of the governing equations in the vicinity of the primary resonance, the gyroscope exhibits multi-response solution; Applying appropriate hopping voltages, the micro beam is pushed into the attraction basin of the response with higher amplitude and accordingly the sensitivity of the gyroscope is enhanced. The proposed gyroscope not only has the capability of having improved sensitivity but also its operating frequency can be tuned both in forward and backward directions by means of applying appropriate piezoelectric voltage with an appropriate polarity.
PubDate: 2019-04-03

• Performance of the Eye-Safe LRS and Color CCD Camera Under Aerosol
Environments
• Abstract: Abstract In this paper, we performed an experiment to compare the observation performance of color CCD camera and the distance measurement characteristics of the eye-safe laser range scanner in a non-visible environment, which simulated atmospheric environments in the reactor building when a severe accident occurred at the nuclear power plant. For the comparison experiments, we fabricated a fog room. Fog particles are sprayed into the fog room until they reach a certain concentration at the time of the severe accident. In this paper, different methods are used to evaluate the performance of the camera and the eye-safe laser range scanner. To verify the observation performance of the color CCD camera under the dense aerosol environments, we calculated contrast from the observation image captured by the camera. And to evaluate characteristics of the eye-safe laser range scanner module, we reviewed the range measurement error under the same visibility constraint situations. In comparative tests, the CCD camera used is a general purpose model with a high magnification zoom lens. We evaluated a model with an effective measuring distance of 30 m, referring to the eye-safe laser range scanner module mounted on the Quince robot III. A visibility was introduced as quantitative evaluation parameter for comparing the monitoring performance of the color CCD camera and range measurement error of an eye-safe LRS module under aerosol environments where the fog particles are sprayed in the fog room. The visibility was calculated by attenuation of the He–Ne laser intensity in the fog room where the fog particles are being sprayed.
PubDate: 2019-03-26

• Handwritten Tifinagh Characters Recognition Using Deep Convolutional
Neural Networks
• Abstract: Abstract Handwritten characters recognition is a challenging task due to the diversity of writing styles. In the present paper, a new offline recognition system based on deep convolutional neural networks (CNNs) is designed. It uses CNNs to extract features from raw pixels which makes the proposed technique more flexible than the other conventional ones that require a set of additional preprocessing steps to extract the desired invariants features. The proposed system is tested on the AMHCD data set and achieved the best recognition accuracy (99.10%) compared to the state-of-the-art results.
PubDate: 2019-03-22

• A Mathematical Analysis Method of the Relationship Between DFT Magnitude
and Periodic Feature of a Signal
• Abstract: Abstract In this study, we developed a logical and complete mathematical analysis of the relationship between the discrete Fourier transform (DFT) magnitude and periodic feature of a signal. The physical meaning of the DFT magnitude index corresponds to the periodic signal feature; further analysis makes clear the relationship among DFT magnitude, magnitude index, and number of samples. The proposed analysis method also elucidates the relationship between alternating current magnitude index and frequency, by the way, the unit of image frequency will be given. The proposed analysis method is suitable for the machine vision and image processing in general.
PubDate: 2019-03-19

• An Effective Approach for Sub-acute Ischemic Stroke Lesion Segmentation by
Adopting Meta-Heuristics Feature Selection Technique Along with Hybrid
Naive Bayes and Sample-Weighted Random Forest Classification
• Abstract: Abstract In Earth, the most crucial illness for cause of death is ischemic stroke. Ischemic stroke arises as a result of an obstacle within a blood vessel supplying blood to the brain. In this paper, for sub-acute ischemic stroke lesion segmentation, we utilize an effective meta-heuristic feature selection technique along with hybrid Naive Bayes (NB) and sample weighted random forest (SWRF) classification approach. Initially, the features are extracted from the pre-processed image, after that, the feature selection is done by using the multi-objective enhanced firefly algorithm. To improve the classification performance, the dimensionality of the feature vectors and errors are reduced by eliminating such irrelevant and redundant features. After the feature selection process, an ensemble of NB and SWRF classifiers is used for segmenting the image. Here the NB classifier is trained and applied to estimate the weights of training samples. Then, the training samples with estimated weights are utilized to train SWRF. In our work stroke lesion segmentation is formulated as a binary classification problem where every local region is classified as either affected or non-affected area.
PubDate: 2019-03-08

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