We gratefully thank Wuhan Urban Construction Group 2023 Annual Scientific Research Project (NO.7) for funding this work.

1. Concrete sample image acquisition
<ol>
	<li>Transport concrete to the workplace, where it will be poured by the pump truck.</li>
	<li>To capture images, switch on the shooting equipment by moving the power key switch to the right and turning it to the ON position. Adjust the camera&#39;s mode knob to the green automatic mode, ensuring the camera lens is parallel to the concrete surface, and press the Shutter Key. Capture 20 image samples of non-vibrated concrete, saving them in .jpg format with an acquisition resolution of 1024 x 1024 pixels, as illustrated in Figure 1.</li>
	<li>Insert the vibrating part of the concrete vibration equipment (plug-in concrete vibrating rod) into the concrete. Connect the power supply, and then switch on the concrete vibration equipment by setting it to the ON position.</li>
	<li>When there is some collapse on the surface of the concrete (caused by the vibration discharging air inside the concrete, leading to the sinking of surface aggregates to fill the gaps, resulting in concrete collapse) and some cement slurry appears, keep the camera lens parallel to the concrete surface and press the Shutter Key. Collect 20 image samples of vibrating concrete, as shown in Figure 2.</li>
	<li>Continue operating the vibration equipment. When there is no obvious collapse on the concrete surface, no cement slurry appears, and no bubbles are generated, stop the vibration process, and capture 20 image samples of vibrated concrete, as shown in Figure 3.</li>
</ol>
2. Sample image gray binarization
<ol>
	<li>Utilize the imread() function of MATLAB software to read the .jpg file as the unit format data of 1024 pixels x 1024 pixels x 3 color channels, representing the red, green, and blue channel values of the image.</li>
	<li>Next, apply the MATLAB function rgb2gray to convert the image to grayscale, denoted by the format gray value = rgb2gray(jpg file name). Calculate the gray value of each pixel according to Equation (1), and save the gray value as the unit8 format data of 1024 x 1024. 
	GR(i,j) = 0.2989R(i,j) + 0.587G(i,j) + 0.114B(i,j)&#160; &#160; (1) 
	where, GR(i,j) is the gray value of the pixel point, R(i,j)&#160;is the red channel value of the pixel point, G(i,j) is the green channel value of the pixel point, and B(i,j)&#160; is the blue channel value of the pixel point.</li>
	<li>Alter the binary value of the gray values greater than the pixel threshold &#952; to 1 and set the binary value of the gray values less than the &#952; pixel to 0.</li>
	<li>After obtaining the binarized gray results, save the outcome as 1024 x 1024 logical format data. In this case, &#952; represents the binarization threshold, and its values are 50, 100, 150, and 200 for different vibration state concrete sample images, as depicted in Figure 5, Figure 6, and Figure 7.</li>
</ol>
3. Calculation of directional gradient eigenvalue
<ol>
	<li>Calculate the horizontal and vertical binary gradient of each pixel in the image using the following equation28 
	Tp = R(x, y + 1) - R(x, y - 1), Th = R(x+1, y) - R(x-1, y) 
	where Th is the horizontal binarized gradient, Tp is the vertical binarized gradient, R is the binarized logical format data, x is the row number of the binarized matrix, and y is the column number of the binarized matrix.</li>
	<li>Calculate the binary gradient direction and size of each point using the following equation29 
	<img alt="Equation 1" src="/files/ftp_upload/65731/65371eq01.jpg" style="margin: auto;" /> 
	where T is the binary gradient size, &#945;T is the binary gradient direction, Th is the horizontal binary gradient, and Tp is the vertical binary gradient.</li>
	<li>Determine the size of the image segmentation block, denoted as n, where the value of n is [ 1,9]. Set the segmentation line for each n pixel in the y direction along the x direction, effectively dividing the image into n x n square blocks based on the segmentation line&#39;s position. Any parts of the image that cannot form complete square blocks are then removed.</li>
	<li>Partition binary gradient direction&#160;&#945;T (value of the binary gradient direction &#945;T is [ 0&#176;, 360&#176;]) into m parts, resulting in m directional gradient statistical angle intervals. Proceed to calculate the gradient statistical value for each block&#39;s gradient statistical angle interval.
	<ol>
		<li>Based on the binary gradient direction of each pixel in the block, classify the pixels into the appropriate gradient statistical angle interval of each direction.</li>
		<li>Sum the binary gradient of the pixels in the gradient statistical angle interval of each direction in the counterclockwise direction to obtain the gradient statistical value of that interval. The obtained results for gradient statistical angle interval directional gradient statistics are depicted in Figure 8, Figure 9, and Figure 10 for block sizes n equal to 8, 128, and 512, respectively .</li>
	</ol>
	</li>
</ol>
4. Constructing directional gradient feature vector
<ol>
	<li>Divide samples into required calculation areas, where each calculation area consists of four adjacent blocks based on the block results obtained in step 3.3. For instance, considering 16 x 16 resolution pixels and a block size of 4 x 4, the image is divided into (16 / 4-1) x ( 16 / 4-1 ) = 9 calculation areas.</li>
	<li>Calculate the statistical value of the directional gradient within angle interval of the gradient statistics of each block in the calculation area. Subsequently, obtain the feature vector with the directional gradient statistics as the component.</li>
	<li>Combine the directional gradient feature vectors from each calculation area to obtain the image&#39;s directional gradient feature vector.</li>
</ol>
5. SVM training
<ol>
	<li>Randomly select 42 samples from the three vibration states to create the training group, leaving the remaining 18 samples as the test group.</li>
	<li>Utilize the fitcecoc function of MATLAB for SVM training; the format is 
	SVM = fitcecoc (trainingFeatures, Trainingeigenvalue) 
	where SVM is the support vector machine to be trained, trainingFeatures is the training group image directional gradient feature vector, and Trainingeigenvalue is the characteristic value of the vibration state of the training group. The characteristic values of the vibration state of the non-vibrated concrete, vibrating concrete, and vibrated concrete samples are 1, 2, and 3, respectively.</li>
	<li>Save the trained SVM using the Save function in .mat format.</li>
</ol>
6. Verification of SVM recognition accuracy
<ol>
	<li>Using MATLAB&#39;s prediction function, input the test group sample image&#39;s directional gradient feature vector into the trained SVM to obtain the calculated value of the vibration state feature for each test sample. The format is as follows: 
	testgroupcalculateseigenvalues = predict (SVM, testFeature) 
	where testgroupcalculateseigenvalues is the calculated value of the vibration state feature, and SVM is the support vector machine trained in step 5. testFeature is the directional gradient feature vector of the test group sample image.</li>
	<li>Determine the test set sample recognition results by feeding the test set into the trained SVM. Count the number of samples for which the test set recognition results match the actual state, and then calculate the recognition accuracy by dividing the number of correct samples by the total number of test set samples.
	<ol>
		<li>If the test recognition accuracy is higher than 94%, consider the SVM recognition as effective. If it is lower than 94%, return to step 1.1 and adjust the binarization threshold, block size n, and the number of directional gradient statistical intervals m.</li>
	</ol>
	</li>
</ol>

Directional Gradient Histogram and SVM for Identifying Concrete Vibration States

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The authors have nothing to disclose.

This paper utilizes the support vector machine (SVM) to learn the image features of various concrete vibration state samples. Based on the machine learning outcomes, a concrete vibration state recognition method based on image recognition is proposed. To enhance the recognition accuracy, it is crucial to control the parameters of the three key steps: image segmentation, image binarization, and directional gradient eigenvalue extraction. According to the test results, a smaller binarization threshold is employed to prepro...

Concrete is a fundamental building material extensively used in the construction industry. During pumping, the concrete frequently develops voids that require compaction through vibration. Inadequate vibration may result in a honeycombed concrete surface, while excessive vibration can lead to concrete segregation1,2. The quality of vibration operation significantly impacts the strength3,4,5,6 and durability of the formed concrete structures7,8. Cai et al.9,10 conducted a study that combined experimental research with numerical analysis to investigate the influence mechanism of vibration on aggregate settlement and concrete durability. The findings revealed that vibration time and aggregate particles exert a substantial impact on aggregate settlement, while aggregate density and the plastic viscosity of the cement-based material have minimal effects. Vibration causes aggregate deposition at the bottom of the concrete specimens. Moreover, as the vibration time increases, the chloride ion concentration decreases at the bottom of the concrete specimens while significantly increasing at the top9,10.
Currently, the assessment of concrete vibration state relies predominantly on manual judgment. As the construction industry continues to progress through intelligent reforms, robot operations have emerged as the future direction11,12. Consequently, a crucial challenge in intelligent vibration operations is how to enable robots to identify the vibration state of concrete.
The histogram of the oriented gradient is a technique that utilizes the intensity gradient of pixels or the distribution of edge directions as a descriptor to characterize the representation and shape of objects in images13,14. This approach operates on the local grid cells of the image, providing robust stability in characterizing image changes under various geometric and optical conditions.
Zhou et al.15 proposed a method for directly extracting directional gradient features from Bayer mode images. This approach omits numerous steps in calculating the directional gradient by matching the color filter column with the gradient operator, thereby significantly reducing the computational requirements for directional gradient image recognition. He et al.16 utilized the directional gradient histogram as the underlying feature and employed the mean clustering algorithm to classify rail fasteners and determine whether the fasteners are defective. The recognition results indicated that the histogram of the oriented gradient feature exhibited high sensitivity to fastener defects, meeting the needs of railway maintenance and repair. In another study, Xu et al.17 preprocessed face image features using Gabor wavelet filtering and reduced the dimension of feature vectors through binary coding and the HOG algorithm. The average recognition accuracy of the method is 92.5%.
The support vector machine (SVM)18 is used to map the vector into a high-dimensional space and establishes a separating hyperplane with a suitable direction to maximize the distance between two parallel hyperplanes. This allows for the classification of support vectors19. Scholars have improved and optimized this classification technology, leading to its application in various fields such as image recognition20,21, text classification22, reliability prediction23, and fault diagnosis24.
Li et al.25 developed a two-stage SVM model for seismic failure pattern recognition, focusing on three seismic failure modes. The analysis results indicate that the proposed two-stage SVM method can achieve more than 90% accuracy for the three failure modes. Yang et al.26 integrated an optimization algorithm with the SVM to simulate the relationship between the five ultrasonic parameters and the stress of the loaded concrete. The performance of an unoptimized SVM is unsatisfactory, particularly in the low-stress stage. However, traversing the model optimized by the algorithm yields improved results, albeit with lengthy computation times. In comparison, the particle swarm optimization optimized SVM significantly reduces the calculation time while delivering optimal simulation results. Yan et al.27 employed SVM technology and introduced a precision-insensitive loss function to predict the elastic modulus of high-strength concrete, comparing its prediction accuracy against the traditional regression model and neural network model. The research findings demonstrate that the SVM technology produces a smaller prediction error for elastic modulus compared to other methods.
This paper collects image samples of concrete under various vibration states and describes the concrete&#39;s different states using the directional gradient histogram technique. The directional gradient is employed as a feature vector for training the SVM, and the study focuses on the viability of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. Additionally, the paper analyzes the influence mechanism between three key parameters-binarization threshold, directional gradient statistical block size, and directional gradient statistical interval number-in the feature extraction process of the directional gradient histogram and the recognition accuracy of the SVM.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>camera</td>
			<td>SONY</td>
			<td>A6000</td>
			<td>The sensor size is 23.5x15.6mm, the maximum acquisition resolution is 1440 * 1080, and the effective pixel is 24.3 million.</td>
		</tr>
		<tr>
			<td>concrete</td>
			<td>Wuhan Construction Changxin Technology Development Co., Ltd.</td>
			<td>C30 pumping concrete</td>
			<td>According to the standard of &#39; concrete strength test and evaluation standard &#39; ( GB / T 50107-2010 ), the standard value of cubic compressive strength is 30 MPa pumping concrete.</td>
		</tr>
		<tr>
			<td>Matlab</td>
			<td>MathWorks</td>
			<td>Matlab R2017a</td>
			<td>MATLAB&#39;s programming interface provides development tools for improving code quality maintainability and maximizing performance. 
			It provides tools for building applications using custom graphical interfaces. 
			It provides tools for combining MATLAB-based algorithms with external applications and languages</td>
		</tr>
		<tr>
			<td>Processor&#160;</td>
			<td>Intel</td>
			<td>12th Gen Intel(R) Core (TM) i7-12700H @ 2.30GHz</td>
			<td>64-bit Win11 processor&#160;</td>
		</tr>
	</tbody>
</table>

image recognition and parameter analysis of concrete vibration state based on support vector machine

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.

This protocol aims to analyze how the three-vector calculation parameters of the directional gradient feature affect the accuracy of the SVM in identifying the concrete vibration state. The primary calculation parameters of the directional gradient feature vector include the directional gradient statistical block size, the number of directional gradient statistical angle intervals, and the binary gray threshold. This section uses three main calculation parameters as variables to design the test. The test parameter levels...

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different ...

The protocol described in this paper utilized the directional gradient histogram technique to extract the characteristics of a concrete image samples under various vibration cells. It employs a support vector machine for machine learning, resulting in imaging recognition method with minimal trained sample requirements and low computer performance demands. This approach significantly reduce the number of samples required, and lowers the computer performance requirements.With a laptop equivalent at 2.3 gigahertz central processing units, the recognition process completes the train space differentiation of the supported vector machine within just 50 seconds. The image segmentation below size of 128 projects and 128 projects is utilized. The number of directional vectors for statistical angle inverse is set to 12.In the 224 resolution image process, the best recognition occurrence for machine learning result is achieved.

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849 Views.  Wuhan University. The protocol described in this paper utilized the directional gradient histogram technique to extract the characteristics of a concrete image samples under various vibration cells. It employs a support vector machine for machine learning, resulting in imaging recognition method with minimal trained sample requirements and low computer performance demands. This approach significantly reduce the number of samples required, and lowers the computer performance requirements.With a laptop eq...

Video: Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses triangulation between matching points in two views of the same scene at different angles to compute depth.&#160;However, unlike a stereo-based method, DFP uses a digital video projector to replace one of the cameras1. The projector rapidly projects a known sinusoidal pattern onto the subject, and the surface of the subject distorts these patterns in the camera&#8217;s field of view. Three distorted patterns (fringe images) from the camera can be used to compute the depth using triangulation.
Unlike other 3D measurement methods, DFP techniques lead to systems that tend to be faster, lower in equipment cost, more flexible, and easier to develop.&#160;DFP systems can also achieve the same measurement resolution as the camera.&#160;For this reason, DFP and other digital structured light techniques have recently been the focus of intense research (as summarized in1-5). Taking advantage of DFP, the graphics processing unit, and optimized algorithms, we have developed a system capable of 30&#160;Hz 3D video data acquisition, reconstruction, and display for over 300,000 measurement points per frame6,7.&#160;Binary defocusing DFP methods can achieve even greater speeds8.
Diverse applications can benefit from DFP techniques. Our collaborators have used our systems for facial function analysis9, facial animation10, cardiac mechanics studies11, and fluid surface measurements, but many other potential applications exist.&#160;This video will teach the fundamentals of DFP techniques and illustrate the design and operation of a binary defocusing DFP system.

This video describes the fundamentals of digital fringe projection techniques, which provide dense 3D measurements of dynamically changing surfaces.&#160;It also demonstrates the design and operation of a high-speed binary defocusing system based on these techniques.

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses ...

Focused Ion Beam Fabrication of LiPON-based Solid-state Lithium-ion Nanobatteries for In Situ Testing

Solid-state electrolytes are a promising replacement for current organic liquid electrolytes, enabling higher energy densities and improved safety of lithium-ion (Li-ion) batteries. However, a number of setbacks prevent their integration into commercial devices. The main limiting factor is due to nanoscale phenomena occurring at the electrode/electrolyte interfaces, ultimately leading to degradation of battery operation. These key problems are highly challenging to observe and characterize as these batteries contain multiple buried interfaces. One approach for direct observation of interfacial phenomena in thin film batteries is through the fabrication of electrochemically active nanobatteries by a focused ion beam (FIB). As such, a reliable technique to fabricate nanobatteries was developed and demonstrated in recent work. Herein, a detailed protocol with a step-by-step process is presented to enable the reproduction of this nanobattery fabrication process. In particular, this technique was applied to a thin film battery consisting of LiCoO2/LiPON/a-Si, and has further been previously demonstrated by in situ cycling within a transmission electron microscope.

Focused Ion Beam Fabrication of LiPON-based Solid-state Lithium-ion Nanobatteries for In Situ Testing

A protocol for the fabrication of electrochemically active LiPON-based solid-state lithium-ion nanobatteries using a focused ion beam is presented.

Solid-state electrolytes are a promising replacement for current organic liquid electrolytes, enabling higher energy densities and improved safety of ...

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Flexible photodetectors have been intensely studied for the use of curved image sensors, which are a crucial component in bio-inspired imaging systems, but several challenging points remain, such as a low absorption efficiency due to a thin active layer and low flexibility. We present an advanced method to fabricate a flexible phototransistor array with an improved electrical performance. The outstanding electrical performance is driven by a low dark current owing to deep impurity doping. Stretchable and flexible metal interconnectors simultaneously offer electrical and mechanical stabilities in a highly deformed state. The protocol explicitly describes the fabrication process of the phototransistor using a thin silicon membrane. By measuring I-V characteristics of the completed device in deformed states, we demonstrate that this approach improves the mechanical and electrical stabilities of the phototransistor array. We expect that this approach to a flexible phototransistor can be widely used for the applications of not only next-generation imaging systems/optoelectronics but also wearable devices such as tactile/pressure/temperature sensors and health monitors.

We present a detailed method to fabricate a deformable lateral NIPIN phototransistor array for curved image sensors. The phototransistor array with an open mesh form, which is composed of thin silicon islands and stretchable metal interconnectors, provides flexibility and stretchability. The parameter analyzer characterizes the electrical property of the fabricated phototransistor.

Flexible photodetectors have been intensely studied for the use of curved image sensors, which are a crucial component in bio-inspired imaging systems, ...

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Soft pneumatic network actuators have become one of the most promising actuation devices in soft robotics which benefits from their large bending deformation and low input. However, their monotonous bending motion form in two-dimensional (2-D) space keeps them away from wide applications. This paper presents a detailed fabrication method of soft pneumatic network actuators with oblique chambers, to explore their motions in three-dimensional (3-D) space. The design of oblique chambers enables actuators with tunable coupled bending and twisting capabilities, which gives them the possibility to move dexterously in flexible manipulators, to become biologically inspired robots and medical devices. The fabrication process is based on the molding method, including the silicone elastomer preparation, chamber and base parts fabrication, actuator assembly, tubing connections, checks for leaks, and actuator repair. The fabrication method guarantees the rapid manufacturing of a series of actuators with only a few modifications in the molds. The test results show the high quality of the actuators and their prominent bending and twisting capabilities. Experiments of the gripper demonstrate the advantages of the development in adapting to objects with different diameters and providing sufficient friction.

Here we present a fabrication method of soft pneumatic network actuators with oblique chambers. The actuators are capable of generating coupled bending and twisting motions, which broadens their application in soft robotics.

Soft pneumatic network actuators have become one of the most promising actuation devices in soft robotics which benefits from their large bending ...

A Polymer-based Piezoelectric Vibration Energy Harvester with a 3D Meshed-Core Structure

In this study, we fabricated a flexible 3D mesh structure with periodic voids by using a 3D lithography method and applying it to a vibration energy harvester to lower resonance frequency and increase output power. The fabrication process is mainly divided into two parts: three-dimensional photolithography for processing a 3D mesh structure, and a bonding process of piezoelectric films and the mesh structure. With the fabricated flexible mesh structure, we achieved the reduction of resonance frequency and improvement of output power, simultaneously. From the results of the vibration tests, the meshed-core-type vibration energy harvester (VEH) exhibited 42.6% higher output voltage than the solid-core-type VEH. In addition, the meshed-core-type VEH yielded 18.7 Hz of resonance frequency, 15.8% lower than the solid-core-type VEH, and 24.6 &#956;W of output power, 68.5% higher than the solid-core-type VEH. The advantage of the proposed method is that a complex and flexible structure with voids in three dimensions can be relatively easily fabricated in a short time by the inclined exposure method. As it is possible to lower the resonance frequency of the VEH by the mesh structure, use in low-frequency applications, such as wearable devices and house appliances, can be expected in the future.

In this study, we fabricated a flexible 3D mesh structure and applied it to the elastic layer of a bimorph cantilever-type vibration energy harvester for the purpose of lowering resonance frequency and increasing output power.

In this study, we fabricated a flexible 3D mesh structure with periodic voids by using a 3D lithography method and applying it to a vibration energy ...

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It is intended as a practical and reproducible procedure to identify macroscopic cracks at an early stage and monitor crack propagation during fatigue tests. It consists of strain field measurements at the weld using DIC. Images are taken at fixed load cycle intervals. Cracks become visible in the computed strain field as elevated strains. This way, the whole width of a small-scale specimen can be monitored to detect where and when a crack initiates. Subsequently, it is possible to monitor the development of the crack length. Because the resulting images are saved, the results are verifiable and comparable. The procedure is limited to cracks initiating at the surface and is intended for fatigue tests under laboratory conditions. By visualizing the crack, the presented procedure allows direct observation of macrocracks from their formation until rupture of the specimen.

Digital image correlation is used in fatigue tests on a resonance testing machine to detect macroscopic cracks and monitor crack propagation in welded specimens. Cracks on the specimen surface become visible as increased strains.

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It ...

Deep Neural Networks for Image-Based Dietary Assessment

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and ...

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of constructing an orbital computed tomography (CT) segmentation model based on U-Net. For supervised learning, a labor-intensive and time-consuming process is required. The method of labeling with super-resolution to efficiently mask the ground truth on orbital CT images is introduced. Also, the volume of interest is cropped as part of the pre-processing of the dataset. Then, after extracting the volumes of interest of the orbital structures, the model for segmenting the key structures of the orbital CT is constructed using U-Net, with sequential 2D slices that are used as inputs and two bi-directional convolutional long-term short memories for conserving the inter-slice correlations. This study primarily focuses on the segmentation of the eyeball, optic nerve, and extraocular muscles. The evaluation of the segmentation reveals the potential application of segmentation to orbital CT images using deep learning methods.

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised learning.

Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of ...

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

There is considerable scientific interest in understanding the strains that tendon cells experience in situ and how these strains influence tissue remodeling. Based on this interest, several analytical techniques have been developed to measure local tissue strains within tendon explants during loading. However, in several cases, the accuracy and sensitivity of these techniques have not been reported, and none of the algorithms are publicly available. This has made it difficult for the more widespread measurement of local tissue strains in tendon explants. Therefore, the objective of this paper was to create a validated analysis tool for measuring local tissue strains in tendon explants that is readily available and easy to use. Specifically, a publicly available augmented-Lagrangian digital image correlation (ALDIC) algorithm was adapted for measuring 2D strains by tracking the displacements of cell nuclei within mouse Achilles tendons under uniaxial tension. Additionally, the accuracy of the calculated strains was validated by analyzing digitally transformed images, as well as by comparing the strains with values determined from an independent technique (i.e., photobleached lines). Finally, a technique was incorporated into the algorithm to reconstruct the reference image using the calculated displacement field, which can be used to assess the accuracy of the algorithm in the absence of known strain values or a secondary measurement technique. The algorithm is capable of measuring strains up to 0.1 with an accuracy of 0.00015. The technique for comparing a reconstructed reference image with the actual reference image successfully identified samples that had erroneous data and indicated that, in samples with good data, approximately 85% of the displacement field was accurate. Finally, the strains measured in mouse Achilles tendons were consistent with the prior literature. Therefore, this algorithm is a highly useful and adaptable tool for accurately measuring local tissue strains in tendons.

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

This paper describes an open-source digital image correlation algorithm for measuring local 2D tissue strains within tendon explants. The accuracy of the technique has been validated using multiple techniques, and it is available for public use.

There is considerable scientific interest in understanding the strains that tendon cells experience in situ and how these strains influence tissue ...

Micro-Embossed Fabrication of Nanocellulose Paper-Based Analytical Devices  

Microembossing: A Convenient Process for Fabricating Microchannels on Nanocellulose Paper-Based Microfluidics

Nanopaper, derived from nanofibrillated cellulose, has generated considerable interest as a promising material for microfluidic applications. Its appeal lies in a range of excellent&#160;qualities, including an exceptionally smooth surface, outstanding optical transparency, a uniform nanofiber matrix with nanoscale porosity, and customizable chemical properties. Despite the rapid growth of nanopaper-based microfluidics, the current techniques used to create microchannels on nanopaper, such as 3D printing, spray coating, or manual cutting and assembly, which are crucial for practical applications, still possess certain limitations, notably susceptibility to contamination. Furthermore, these methods are restricted to the production of millimeter-sized channels. This study introduces a straightforward process that utilizes convenient plastic micro-molds for simple microembossing operations to fabricate microchannels on nanopaper, achieving a minimum width of 200 &#181;m. The developed microchannel outperforms existing approaches, achieving a fourfold improvement, and can be fabricated within 45 min. Furthermore, fabrication parameters have been optimized, and a convenient quick-reference table is provided for application developers. The proof-of-concept for a laminar mixer, droplet generator, and functional nanopaper-based analytical devices (NanoPADs) designed for Rhodamine B sensing using surface-enhanced Raman spectroscopy was demonstrated. Notably, the NanoPADs exhibited exceptional performance with improved limits of detection. These outstanding results can be attributed to the superior optical properties of nanopaper and the recently developed accurate microembossing method, enabling the integration and fine-tuning of the NanoPADs.

Author Spotlight: Revolutionizing Microfluidics Through Microchannel Fabrication on Nanopaper 

This protocol describes a straightforward process that utilizes convenient plastic micro-molds for simple microembossing operations to fabricate microchannels on nanofibrillated cellulose paper, achieving a minimum width of 200 &#181;m.

Nanopaper, derived from nanofibrillated cellulose, has generated considerable interest as a promising material for microfluidic applications. Its appeal ...