Journal: Sensors (Basel, Switzerland)

Article Title: Wireless Communication Test on 868 MHz and 2.4 GHz from inside the 18650 Li-Ion Enclosed Metal Shell

doi: 10.3390/s22051966

Figure Lengend Snippet: MATLAB RF test setup initial azimuth and elevation planes polar plots for 868 MHz and 2.4 GHz communication with the SMA as radiative element ( A ), the wire loop connected to the SMA ( B ) and the modification on the 3D design for the SMA to insert the simulation RF excitation source ( C ). Since the radiation pattern for dipole and loop antenna are resembling in shape with a torus while 90° offset from each other, the azimuth (az) plane for SMA is similar with the elevation (el) plane for the loop.

Article Snippet: By using the 3D MATLAB simulation, it was possible to illustrate how microwave antennas would be affected by small restrictive metal environments, such as the 18650-cell shell.

Techniques: Modification

Journal: Sensors (Basel, Switzerland)

Article Title: Wireless Communication Test on 868 MHz and 2.4 GHz from inside the 18650 Li-Ion Enclosed Metal Shell

doi: 10.3390/s22051966

Figure Lengend Snippet: MATLAB signal attenuation simulation by using the Three-ray and Free space models for 868 MHz and 2.4 GHz for following the scenario described in ( A ), resulting in a computed reflection coefficient versus distance for the concrete ground and ceiling such illustrated in ( B ).

Article Snippet: By using the 3D MATLAB simulation, it was possible to illustrate how microwave antennas would be affected by small restrictive metal environments, such as the 18650-cell shell.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Wireless Communication Test on 868 MHz and 2.4 GHz from inside the 18650 Li-Ion Enclosed Metal Shell

doi: 10.3390/s22051966

Figure Lengend Snippet: MATLAB 3D radiation pattern simulation for the cell’s shell radiative structure integrating the SMA and the wired loop for 868 MHz and 2.4 GHz over the ground plane in a vertical orientation.

Article Snippet: By using the 3D MATLAB simulation, it was possible to illustrate how microwave antennas would be affected by small restrictive metal environments, such as the 18650-cell shell.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Wireless Communication Test on 868 MHz and 2.4 GHz from inside the 18650 Li-Ion Enclosed Metal Shell

doi: 10.3390/s22051966

Figure Lengend Snippet: MATLAB 3D radiation pattern simulation for the cell’s shell radiative structure integrating the SMA and the wired loop for 868 MHz and 2.4 GHz over the ground plane in a horizontal orientation.

Article Snippet: By using the 3D MATLAB simulation, it was possible to illustrate how microwave antennas would be affected by small restrictive metal environments, such as the 18650-cell shell.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Wireless Communication Test on 868 MHz and 2.4 GHz from inside the 18650 Li-Ion Enclosed Metal Shell

doi: 10.3390/s22051966

Figure Lengend Snippet: MATLAB signal attenuation simulation by using the Three-Ray and Free space models for 868 MHz ( A ), and 2.4 GHz ( B ), between the concrete ground.

Article Snippet: By using the 3D MATLAB simulation, it was possible to illustrate how microwave antennas would be affected by small restrictive metal environments, such as the 18650-cell shell.

Techniques:

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Graphical representation of our proposed pipeline workflow for automated generation and risk assessment of CT images. ( A ): Initial reference data can be either a set of real CT-data generated using high-dose radiation or artificially simulated data. ( B ) Exemplary high-quality and low-quantum noise image of lung vessels. ( C ) exemplary low-dose CT images with high quantum noise. ( D ) Workflow from image generation to subsequent benchmarking of ML/DL-denoising methods. Starting with high-quality data or artificially generated reference data, respectively, a spectrum of image noise σ is added for a multitude of combinations from patient-specific and CT control variables, as suggested in Equation . The noisy images are then denoised using various state-of-the-art methods and the processed images are compared to the original reference data.

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques: Generated, Control

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Graphical overview of the Probabilistic Mumford–Shah (PMS) framework. ( A ) Summary of the parameters and variables. ( B ) Core rSPA algorithm idea: 3D-denoising with the regularized Scalable Probabilistic Approximation algorithm (rSPA). Given the (noisy) CT voxel data V , rSPA minimizes the function L ( C , Γ ) and seeks for the optimal segmentation of V in terms of the K spatially-persistent latent features characterized by the latent feature probabilities in K rows of the matrix Γ , as well as by the latent colors as K columns of the latent color matrix C . Persistency of the feature segmentation is imposed by the second term of the right-hand side of the function L ( C , Γ ) , which penalizes the differences in feature probability values in the spatially-neighboring points. ( C ) Denoising idea: latent feature probabilities are persistent (slowly-changing) 3D functions. ( D ) Graphical representation of the overlapping domain decomposition used in the parallel DD-rSPA algorithm.

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques:

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Radiation exposure, quantum noise, and denoising performance of CNNs and rSPA in low-radiation and ultra-low-radiation thorax CT regimes. ( A ) Reference data of a thorax CT voxel fragment (approx. 5 cm 3 ) of a 19-year-old female with the BMI 27.5, acquired with the Somatum Emotion 16 2007 (Siemens Aktiengesellschaft, Berlin, Germany) at 130 kV tube voltage. ( B ) Simulated decrease in the radiation exposure CTDI vol from 15.6 mGy (reference frame) to 3.3 mGy (for low-radiation simulations) and 0.5 mGy (ultra-low-radiation) results in a significant increase of quantum noise. ( C ) Reconstructed images using CNNs. ( D ) Reconstructed images using rSPA. ( E ) 3D segmentation of the original reference frame. ( F ) 3D segmentation based on the images denoised using CNNs. ( G ) 3D-segmentation of the images denoised by rSPA.

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques:

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Comparing denoising performance on synthetic CT images of noisy circles, with DL from additionally trained to recognize circles for non-Gaussian noise model: ( A ) medium noise scenario, corresponding to low-radiation regime with around 3.3 mGy; ( B ) high noise scenario, corresponding to ultra-low-radiation regime with 0.5 mGy.

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques:

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Comparing denoising quality, cost and parallelizability: ( A – C ) comparison of PMS rSPA algorithm to the regularized Mumford–Shah denoising tool introduced in and to the additionally trained DL denoising algorithm from and ; ( D , E ) computational cost scaling and performance for DL (without taking into account time for additional training), sequential rSPA, parallel DD-rSPA and DD-rSPA followed by DL. Each point of each method’s curve and surface is obtained from statistical averaging of the respective values obtained by analyzing 10 randomly-generated images with these particular combinations of image size and noise level.

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques: Comparison, Generated

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Comparing CT image denoising performances for three CT noise models: ( A ) additive Gaussian noise model (CT noise variance is independent of the feature color); ( B ) multiplicative non-Gaussian noise model (CT noise variance changes with the amplitude of the underlying color signal); ( C ) empirical noise obtained from the thorax CT patient data. In ( A , B ), generation of synthetic images was performed for a patient with a water-equivalent diameter of 30 cm, which is subject to a Thorax CT with a typical tube voltage of 120 kV in the range of tube currents between 5–180 mA and a set of artificial anatomic features from A (with a feature contrast of 200 HU). In ( C ), real patient data were used. Comparison is performed with three primary image quality criteria: with mean squared error (left panels); with peak signal-to-noise ratio (middle panels); and with the 3D multiscale structural similarity index (right panels).

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques: Comparison

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM): ( A ) varying the true underlying feature contrast and LAR for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the true underlying feature contrast and LAR for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm; ( C ) denoising performance comparison when varying the patient size and the effective absorbed radiation dose density, with the 200 Hounsfield Units (HU) feature contrast differences.

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques: Comparison

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Deterioration of CT image quality (decrease in 3D MS-SSIM index, baseline = 100%) caused by a reduction of lifetime attributable risk (LAR) for different methods. The CT scans pertain to the infant patient, with a fixed feature contrast of 200 HU.

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques:

Journal: Journal of Imaging

Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

doi: 10.3390/jimaging8060156

Figure Lengend Snippet: Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM) for simulated thorax CT: ( A ) varying the absorbed radiation dose for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the absorbed radiation dose for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm. Noiseless thorax CT image used as reference in this performance comparison is available at https://www.dropbox.com/s/29x0xivg8l80q10/female_lung_thorax_CT_image_section_v2.mat?dl=0 (accessed on 18 March 2022). Dotted lines show 95% nonparametric confidence intervals (c.i.) obtained for every value of CTDI vol from 100 different independently-generated noisy synthetic CT images, using the MATLAB function quantile() .

Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

Techniques: Comparison, Generated

Journal: Biomicrofluidics

Article Title: Solution pH change in non-uniform alternating current electric fields at frequencies above the electrode charging frequency

doi: 10.1063/1.4904059

Figure Lengend Snippet: Field and medium comparison experiments at 5 kHz and 5.5 Vpp. Columns are organized by methanol and water in uniform and nonuniform DEP electric field configurations. Rows are organized by time with t = 0, 8, and 120 s and intensity difference obtained by MATLAB image analysis. Fluorescein intensity changes are apparent in water in both uniform and non-uniform DEP electric field configurations as emphasized in (n) and (o) while negligible changes were observed in methanol in (m) and (p).

Article Snippet: The respective rows from top to bottom are: first row shows 2-D gray scale image averaged pixel by pixel from 5 experimental repeats (a)–(d), the second row is a 3-D mesh image where the z height corresponds to the intensity magnitude (e)–(h), and the third row is a 3-D mesh of the pixel by pixel standard deviation calculated from the 5 experiments (i)–(l). fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window FIG. 4. caption a7 (a)–(d) Gray-scale 2-D dye intensity plot at t = 0, 8, 80, and 120 s under 5.5 V pp and 5 kHz. (e)–(h) 3-D MATLAB plot of the same data at the same time points. (i)–(l) Calculated standard deviations of 5 repeats of the data in the first two rows. (m) Diagram illustrating regions examined for intensity analysis. (n) Time dependencies of the regions in (m).

Techniques: Comparison

Journal: Biomicrofluidics

Article Title: Solution pH change in non-uniform alternating current electric fields at frequencies above the electrode charging frequency

doi: 10.1063/1.4904059

Figure Lengend Snippet: (a)–(d) Gray-scale 2-D dye intensity plot at t = 0, 8, 80, and 120 s under 5.5 Vpp and 5 kHz. (e)–(h) 3-D MATLAB plot of the same data at the same time points. (i)–(l) Calculated standard deviations of 5 repeats of the data in the first two rows. (m) Diagram illustrating regions examined for intensity analysis. (n) Time dependencies of the regions in (m). Line intensity (solid green) was utilized for all subsequent analysis.

Article Snippet: The respective rows from top to bottom are: first row shows 2-D gray scale image averaged pixel by pixel from 5 experimental repeats (a)–(d), the second row is a 3-D mesh image where the z height corresponds to the intensity magnitude (e)–(h), and the third row is a 3-D mesh of the pixel by pixel standard deviation calculated from the 5 experiments (i)–(l). fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window FIG. 4. caption a7 (a)–(d) Gray-scale 2-D dye intensity plot at t = 0, 8, 80, and 120 s under 5.5 V pp and 5 kHz. (e)–(h) 3-D MATLAB plot of the same data at the same time points. (i)–(l) Calculated standard deviations of 5 repeats of the data in the first two rows. (m) Diagram illustrating regions examined for intensity analysis. (n) Time dependencies of the regions in (m).

Techniques: