This work is supported by 2024 Henan Provincial Higher Education Institutions Key Scientific Research Project Funding Program Establishment (Project Number:24A520053). This study is also supported by Specialized Creation and Integration Characteristic Demonstration Course Construction in Henan Province.

1. Experimental setup and procedure
<ol>
	<li>Load the pre-trained VGG16 model. 
	NOTE: The first step is to load the pre-trained VGG16 model from the Keras library6.
	<ol>
		<li>To load a pre-trained VGG16 model in Python using popular deep learning libraries like PyTorch (see Table of Materials), follow these general steps:
		<ol>
			<li>Import torch. Import torchvision.models as models.</li>
			<li>Load the pre-trained VGG16 model. vgg16_model = models.vgg16(pretrained=True).</li>
			<li>Ensure that the summary of the VGG16 model is &#34;print(vgg16_model)&#34;.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Define the DCL and DEDN models.
	<ol>
		<li>For the pseudo-code of the DCL algorithm, provide Input: Image dataset SOD and Output: Trained DCL model.
		<ol>
			<li>Initialize the DCL model with the VGG16 backbone network.</li>
			<li>Preprocess the image dataset D (e.g., resize, normalization).</li>
			<li>Split the dataset into training and validation sets.</li>
			<li>Define the loss function for training the DCL model (e.g., binary cross-entropy).</li>
			<li>Set the hyperparameters for training: Learning rate (0.0001), Number of training epochs set (50), Batch size is (8), Optimizer (Adam).</li>
			<li>Train the DCL model: for each epoch in the defined number of epochs, do for each batch in the training set. Input the following:
			<ol>
				<li>Forward pass: Feed batch images to the DCL model. Compute the loss using the predicted saliency maps and ground truth maps.</li>
				<li>Backward pass: Update the model parameters using gradient descent end. Compute the validation loss and other evaluation metrics on the validation set end.</li>
			</ol>
			</li>
			<li>Save the trained DCL model.</li>
			<li>Return the trained DCL model.</li>
		</ol>
		</li>
		<li>For pseudo-code for the DEDN algorithm, input: Image dataset (X), Ground truth saliency maps (Y), Number of training iterations (N).
		<ol>
			<li>For the Encoder Network, ensure that the encoder is based on the VGG16 skeleton with modifications (as mentioned below). 
			NOTE: encoder_input = Input(shape=input_shape) 
			encoder_conv1 = Conv2D(64, (3, 3), activation=&#39;relu&#39;, padding=&#39;same&#39;)(encoder_input) 
			encoder_pool1 = MaxPooling2D((2, 2))(encoder_conv1) 
			encoder_conv2 = Conv2D(128, (3, 3), activation=&#39;relu&#39;, padding=&#39;same&#39;)(encoder_pool1) 
			encoder_pool2 = MaxPooling2D((2, 2))(encoder_conv2) 
			encoder_conv3 = Conv2D(256, (3, 3), activation=&#39;relu&#39;, padding=&#39;same&#39;)(encoder_pool2) 
			encoder_pool3 = MaxPooling2D((2, 2))(encoder_conv3)</li>
			<li>For the Decoder Network, ensure that the decoder is based on the VGG16 skeleton with modifications (as mentioned below). 
			NOTE: decoder_conv1 = Conv2D(256, (3, 3), activation=&#39;relu&#39;, padding=&#39;same&#39;)(encoder_pool3) 
			decoder_upsample1 = UpSampling2D((2, 2))(decoder_conv1) 
			decoder_conv2 = Conv2D(128, (3, 3), activation=&#39;relu&#39;, padding=&#39;same&#39;)(decoder_upsample1) 
			decoder_upsample2 = UpSampling2D((2, 2))(decoder_conv2) 
			decoder_conv3 = Conv2D(64, (3, 3), activation=&#39;relu&#39;, padding=&#39;same&#39;)(decoder_upsample2) 
			decoder_upsample3 = UpSampling2D((2, 2))(decoder_conv3) 
			decoder_output = Conv2D(1, (1, 1), activation=&#39;sigmoid&#39;, padding=&#39;same&#39;)(decoder_upsample3)</li>
		</ol>
		</li>
		<li>Define the DEDN model. model = Model (inputs = encoder_input, outputs = decoder_output).</li>
		<li>Compile the model. model.compile (optimizer = adam, loss = binary_crossentropy).</li>
		<li>Select the Training loop. 
		&#8203;NOTE: For iteration in range(N): # Randomly select a batch of images and ground truth maps; batch_X, batch_Y = randomly_select_batch(X, Y, batch_size).
		<ol>
			<li>Train the model on the batch. loss = model.train_on_batch(batch_X, batch_Y). Print the loss for monitoring.</li>
		</ol>
		</li>
		<li>Save the trained model. model.save (&#39;dedn_model.h5&#39;).</li>
	</ol>
	</li>
	<li>Combine.
	<ol>
		<li>Combine the outputs of the DCL and DEDN networks and refine the saliency map using a fully connected conditional random field (CRF) model.</li>
	</ol>
	</li>
</ol>
2. Image processing
<ol>
	<li>Click on run code to bring up the GUI interface (Figure 4).</li>
	<li>Click on open image to select the path and thus the image to be detected.</li>
	<li>Click on the display image to display the image that has been selected for detection.</li>
	<li>Click on start detection to detect the selected image. 
	NOTE: Detection result will appear with the detected image, i.e., the result of the salient object (Figure 5).</li>
	<li>Click on select the save path to save the image results of the salient object detection.</li>
</ol>

Enhanced Precision in Salient Object Detection Using Deep Neural Networks

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The authors have nothing to disclose.

The article introduces an end-to-end deep neural network specifically designed for the detection of salient objects in complex environments. The network is composed of two interconnected components: a pixel-level multiscale fully convolutional network (DCL) and a deep encoder-decoder network (DEDN). These components work synergistically, incorporating contextual semantics to generate visual contrasts within multiscale feature maps. Additionally, they leverage both deep and shallow image features to improve the precision of object boundary delineation. The integration of a fully connected Conditional Random Field (CRF) model further enhances the spatial coherence of saliency maps and contour delineation.
To achieve this objective, two deep networks, namely the Deep Context Learning (DCL) network and the Deep Encoder-Decoder Network (DEDN), were constructed based on the VGG16 architecture. As explained in operational step 1.2, input images processed through the DCL yield feature maps of varying scales, characterized by distinct receptive fields. These maps are subsequently combined with contextual semantics, ultimately generating saliency maps with dimensions W &#215; H, possessing inter-dimensional coherence. Specifically, the DCL11 utilizes a pair of convolutional layers, each equipped with a 7 x 7 kernel, to replace the final pooling layer of the original VGG16 network. This modification is crucial in preserving spatial information within the feature maps. In collaboration with contextual semantics, it results in saliency maps endowed with inter-dimensional coherence.
Simultaneously, the Deep Encoder-Decoder Network (DEDN)14 employs 3 x 3 kernel convolutional layers in its decoder section, followed by a solitary convolutional layer after the last decoding module. This integration of deep and shallow-level features by the DEDN facilitates the generation of saliency maps with spatial dimensions W &#215; H, addressing challenges associated with indistinct object boundaries. The research presented in this article introduces a pioneering technique for salient object detection, as explained in operational step 1.3. It amalgamates the DCL and DEDN models into a unified network framework through a training process that learns the weights of these two deep networks and subsequently merges the acquired saliency maps. Further refinement is achieved by applying Conditional Random Fields (CRF) in a fully connected manner. The primary aim of this refinement is to enhance spatial consistency and contour localization.
Both networks have been enhanced using the VGG16 architecture as their neural network backbone, ultimately fusing through Conditional Random Fields (CRF). While the proposed algorithm demonstrates considerable potential in the domain of target detection within intricate environments, future research initiatives will be directed towards enhancing its computational efficiency. The aim is to achieve superior processing speed without compromising detection performance.
The proposed algorithm undergoes extensive evaluation against 10 contemporary algorithms on the SOD20 and ECSSD21 databases. The evaluation results indicate that the proposed algorithm outperforms other approaches in terms of precision and accuracy, establishing its efficacy in salient object detection within complex environments. Furthermore, the algorithm shows promise for transferability to visual tasks in the field of electric power systems. It holds significant potential for applications such as the segmentation of insulators and early detection of fire hazards in complex environments within intelligent power grids.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Matlab</td>
			<td>MathWorks</td>
			<td>Matlab R2016a</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>11th Gen Intel(R) Core (TM) i5-1135G7 @ 2.40GHz</td>
			<td>64-bit Win11 processor&#160;</td>
		</tr>
		<tr>
			<td>Pycharm</td>
			<td>JetBrains</td>
			<td>PyCharm 3.0</td>
			<td>PyCharm is a Python IDE (Integrated Development Environment) 
			a list of required python: 
			modulesmatplotlib 
			skimage 
			torch 
			os 
			time 
			pydensecrf 
			opencv 
			glob 
			PIL 
			torchvision 
			numpy 
			tkinter</td>
		</tr>
		<tr>
			<td>PyTorch&#160;</td>
			<td>Facebook</td>
			<td>PyTorch 1.4&#160;</td>
			<td>PyTorch is an open source Python machine learning library , based on Torch , used for natural language processing and other applications.PyTorch can be viewed both as the addition of GPU support numpy , but also can be viewed as a powerful deep neural network with automatic derivatives .</td>
		</tr>
	</tbody>
</table>

end-to-end deep neural network for salient object detection in complex environments

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted environments. In light of this pressing concern, this article presents an end-to-end deep neural network that aims to detect salient objects within complex environments. The study introduces an end-to-end deep neural network that aims to detect salient objects within complex environments. Comprising two interrelated components, namely a pixel-level multiscale full convolutional network and a deep encoder-decoder network, the proposed network integrates contextual semantics to produce visual contrast across multiscale feature maps while employing deep and shallow image features to improve the accuracy of object boundary identification. The integration of a fully connected conditional random field (CRF) model further enhances the spatial coherence and contour delineation of salient maps. The proposed algorithm is extensively evaluated against 10 contemporary algorithms on the SOD and ECSSD databases. The evaluation results demonstrate that the proposed algorithm outperforms other approaches in terms of precision and accuracy, thereby establishing its efficacy in salient object detection within complex environments.

This study introduces an end-to-end deep neural network comprising two complementary networks: a pixel-level multi-scale fully convolutional network and a deep encoder-decoder network. The first network integrates contextual semantics to derive visual contrasts from multi-scale feature maps, addressing the challenge of fixed receptive fields in deep neural networks across different layers. The second network utilizes both deep and shallow image features to mitigate the issue of blurred boundaries in target objects. Finally, a fully connected conditional random field (CRF) model is applied to improve the spatial coherence and contours of the saliency map.
The study conducts a qualitative and quantitative comparison between the proposed algorithm and ten existing algorithms in the field. Experimental results demonstrate the effectiveness of the proposed algorithm in enhancing the accuracy of significant object detection. Moreover, the algorithm shows potential applicability in power visual tasks, offering promising prospects in various complex environments within the domain of intelligent power grids.
Ablation experiments 
The current investigation has executed a series of ablation experiments on the SOD database to evaluate the algorithm&#39;s effectiveness. The outcomes of said experiments are detailed in Table 1. (1) The evaluation metrics used to compare the model are the Precision-Recall curve22, F&#946; and EMAE. Table 1 (No. 1) results show that the removal of the DCL model from the algorithm causes a decrease in the F&#946; value and an increase in the EMAE value. The aforementioned observation suggests that Dynamic Convolutional Layers (DCL) may have the capacity to bolster the efficacy of deep neural networks by integrating dynamic receptive fields into diverse layers, which, in turn, can heighten the visual contrast of multi-scaled feature maps. (2) From Table 1 (No.2), we can see that the algorithm in this paper only deletes the DEDN structure, comparing with the complete module in Table 1 (No.3), the F_&#946; value in Table 1 (No.2) decreases and the E_MAE value increases, which indicates that the DEDN can effectively and accurately locate the prominence, increase the receptive field, and retain the detailed information of the image, while reinforcing the boundaries of the prominence.
Figure 6 presents the visualization results of the ablation experiment. The images are arranged from left to right, showcasing the original image, the DCL algorithm result, the DEDN algorithm result, the proposed algorithm in this paper, and the corresponding ground-truth image. Upon closer inspection of Figure 6, it is evident that the DCL algorithm tends to describe the target boundary when detecting images in the SOD database but struggles to effectively filter the background. The DEDN algorithm, on the other hand, strengthens the target boundary but faces challenges in suppressing background redundancy information. In contrast, the algorithm proposed in this paper combines the strengths of these two algorithms in a complementary manner, effectively highlighting the target while suppressing redundancy information from complex backgrounds. The results of this paper surpass those of either algorithm alone.
Comparison with other advanced algorithms 
To evaluate the performance of the proposed algorithm, a comparative analysis was conducted with eleven prominent salient object detection methods, namely GMR23, GS24, SF25, PD26, SS27, DRFI28, MDF29, ELD30, DHS31, and DCL11. Among them, GMR23, GS24, SF25, PD26, SS27, and DRFI28 are well-performing traditional unsupervised saliency detection methods commonly employed as benchmarks by many deep saliency models. The remaining four methods leverage deep convolutional neural networks and have demonstrated superior performance in their respective research literature. The evaluation metrics employed for this study include PR curves, maximum F-measure values, and mean absolute error (MAE). The selected test datasets comprise SOD and ECSSD datasets.
Quantitative comparison 
Figure 7 illustrates the precision-recall (PR) curves comparing the algorithm proposed in this study with 10 other prominent salient object detection methods on the SOD and ECSSD publicly available image datasets. The curves clearly indicate that the algorithm proposed in this study outperforms the other 10 algorithms, thereby validating the superior detection performance of the method presented in this paper. Of particular note is this algorithm&#39;s ability to sustain high precision even as the recall approaches 1, indicating its accurate segmentation of visually salient objects while ensuring their integrity. Table 2 provides a quantitative comparison of the methods on the SOD and ECSSD test datasets, revealing that our algorithm achieves better performance in terms of the maximum F-measure (F&#946;) and mean absolute error (EMAE), primarily attributed to the complementary combination of the DCL network and the DEDN network.
Qualitative comparison 
Furthermore, a qualitative assessment was conducted to juxtapose the visual outcomes of the analyzed techniques, as illustrated in Figure 8. These figures showcase a sequence of images arranged from left to right, starting with the original images, followed by GMR23, GS24, SF25, PD26, SS27, DRFI28, MDF29, ELD30, DHS31, and DCL11, the algorithm proposed in this article and the Ground-truth map.
In Figure 8A, a qualitative comparison in the SOD dataset is presented. It is evident that the original image in column 1 exhibits a relatively similar color distribution between the background and foreground elements. Additionally, the first and third saliency maps present a more intricate background texture, potentially impeding the detection of the salient object. The algorithm outlined in this study shows a significant improvement in the detection of salient objects in complex environments, surpassing the performance of other existing algorithms. The initial image in column 1, particularly the second image, contains background branches that are intermingled with the foreground animal, posing a challenge for correct assessments of the foreground object. The algorithm put forth in this study successfully addresses the issue of background interference and effectively highlights the foreground object region. The experiment demonstrates that the algorithm proposed achieves high accuracy and precision in dealing with images with complex backgrounds.
In Figure 8B, a qualitative comparison within the ECSSD dataset is presented, showcasing the visual contrast results of various salient object detection methods. The findings indicate that the proposed algorithm achieves superior detection performance across diverse and complex natural scenes. These natural scene images encompass scenarios such as salient objects in contact with image boundaries in the first and second images and low contrast and color similarity between the foreground and background in the third image. Through these visualized outcomes, the algorithm proposed here effectively highlights complete salient objects while ensuring clear object boundaries. Regardless of the scale of salient objects, whether they are large or small, the algorithm consistently exhibits high segmentation accuracy, validating its effectiveness. Furthermore, compared to other methods, the algorithm presented in this study demonstrates higher robustness, mitigating false detections in saliency regions (or background regions).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65554/65554fig1v3.jpg" /> 
Figure 1: Overall structure framework. Schematic representation of the proposed model. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig1v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65554/65554fig2v3.jpg" /> 
Figure 2: The pixel-level DCL model. A pixel-level DCL model is used within the architecture of VGG16, a deep convolutional neural network. The initial four maximum pooling layers are interconnected with three kernels. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65554/65554fig3v2.jpg" /> 
Figure 3: Encoding-decoding network model (DEDN). Deep features are fused with shallow features inspired by the edge detection work, proposing the encoder-decoder network model (DEDN). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65554/65554fig04.jpg" /> 
Figure 4: The GUI interface. The code is run to generate a GUI interface for easy operation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65554/65554fig05.jpg" /> 
Figure 5: GUI interface demo. GUI interface presentation for easy comparison of test image results. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65554/65554fig06.jpg" /> 
Figure 6: The visualization results of the ablation experiment. (A) The original image, (B) the DCL algorithm, (C) the DEDN algorithm, (D) the algorithm used in the current study, and (E) the corresponding ground-truth image are shown from left to right. As can be seen from Figure 6, (B) the DCL algorithm can only describe the target boundary when detecting images, and the background is difficult to filter. (C) The DEDN algorithm has the effect of strengthening the target boundary, but it is equally difficult to suppress the background redundancy information; while (D) the algorithm in this paper combines these two algorithms in a complementary way, highlighting the target while suppressing the redundancy information of the complex background. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65554/65554fig07.jpg" /> 
Figure 7:&#160;P-R curve. (A) P-R curves are in SOD databases and (B) P-R curves are in ECSSD databases respectively. The P-R curves of this study&#39;s algorithm in both (A) and (B) are higher than those of the other 10 algorithms, which is proved that the algorithm in this paper has high accuracy relative to these 10 algorithms. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65554/65554fig08.jpg" /> 
Figure 8: Qualitative comparison. Qualitative comparison of different salient object detection algorithms in the SOD databases (A) and ECSSD databases (B) respectively.The original input images are presented from left to right, GMR, GS, SF, PD, SS, DRFI, MDF, ELD, DHS, and DCL, the algorithm proposed in this study and the Ground-truth map. As seen in (A), the algorithm outlined provides a significant improvement in the detection of salient objects in complex environments, surpassing the performance of other existing algorithms. As can be seen in (B), the algorithm proposed in this study has higher robustness compared to other methods because it reduces the false detection of salient (or background) regions. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65554/65554fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>No.</td>
			<td>Module setting</td>
			<td>F&#946;</td>
			<td>EMAE</td>
		</tr>
		<tr>
			<td>NO.1</td>
			<td>Removing DCL only</td>
			<td>0.835</td>
			<td>0.117</td>
		</tr>
		<tr>
			<td>NO.2</td>
			<td>Removing DEDN only</td>
			<td>0.832</td>
			<td>0.126</td>
		</tr>
		<tr>
			<td>NO.3</td>
			<td>Complete Module</td>
			<td>0.854</td>
			<td>0.110</td>
		</tr>
	</tbody>
</table>
Table 1:&#160;Results of ablation experiments.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Model</td>
			<td colspan="2">SOD</td>
			<td colspan="2">ECSSD</td>
		</tr>
		<tr>
			<td>F&#946;</td>
			<td>EMAE</td>
			<td>F&#946;</td>
			<td>EMAE</td>
		</tr>
		<tr>
			<td>GMR</td>
			<td>0.740</td>
			<td>0.148</td>
			<td>0.476</td>
			<td>0.189</td>
		</tr>
		<tr>
			<td>GS</td>
			<td>0.677</td>
			<td>0.188</td>
			<td>0.355</td>
			<td>0.344</td>
		</tr>
		<tr>
			<td>SF</td>
			<td>0.779</td>
			<td>0.150</td>
			<td>0.309</td>
			<td>0.230</td>
		</tr>
		<tr>
			<td>PD</td>
			<td>0.720</td>
			<td>0.162</td>
			<td>0.358</td>
			<td>0.248</td>
		</tr>
		<tr>
			<td>SS</td>
			<td>0.574</td>
			<td>0.225</td>
			<td>0.268</td>
			<td>0.344</td>
		</tr>
		<tr>
			<td>DRFI</td>
			<td>0.801</td>
			<td>0.127</td>
			<td>0.516</td>
			<td>0.166</td>
		</tr>
		<tr>
			<td>MDF</td>
			<td>0.709</td>
			<td>0.150</td>
			<td>0.832</td>
			<td>0.105</td>
		</tr>
		<tr>
			<td>ELD</td>
			<td>0.737</td>
			<td>0.154</td>
			<td>0.869</td>
			<td>0.078</td>
		</tr>
		<tr>
			<td>DHS</td>
			<td>0.812</td>
			<td>0.127</td>
			<td>0.907</td>
			<td>0.059</td>
		</tr>
		<tr>
			<td>DCL</td>
			<td>0.786</td>
			<td>0.131</td>
			<td>0.901</td>
			<td>0.068</td>
		</tr>
		<tr>
			<td>This study</td>
			<td>0.854</td>
			<td>0.110</td>
			<td>0.938</td>
			<td>0.044</td>
		</tr>
	</tbody>
</table>
Table 2: The maximum F-measure values (F&#946;) and MAE (Mean Absolute Error) values of various algorithms across two image datasets.

Watch this Scientific Journal Video about End-To-End Deep Neural Network for Salient Object Detection in Complex Environments at JoVE.com

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit ...

end-to-end-deep-neural-network-for-salient-object-detection-complex

Nanyang Technological University

Research

JoVE Journal

Engineering

423 Views.  Zhengzhou University of Economics and Business. The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.

Video: Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

For aqueous based supramolecular polymers, the simultaneous control over shape, size and stability is very difficult1. At the same time, the ability to do so is highly important in view of a number of applications in functional soft matter including electronics, biomedical engineering, and sensors. In the past, successful strategies to control the size and shape of supramolecular polymers typically focused on the use of templates2,3, end cappers4 or selective solvent techniques5. 
Here we disclose a strategy based on self-assembling discotic amphiphiles that leads to the control over stack length and shape of ordered, chiral columnar aggregates. By balancing electrostatic repulsive interactions on the hydrophilic rim and attractive non-covalent forces within the hydrophobic core of the polymerizing building block, we manage to create small and discrete spherical objects6,7. Increasing the salt concentration to screen the charges induces a sphere-to-rod transition. Intriguingly, this transition is expressed in an increase of cooperativity in the temperature-dependent self-assembly mechanism, and more stable aggregates are obtained. 
For our study we select a benzene-1,3,5-tricarboxamide (BTA) core connected to a hydrophilic metal chelate via a hydrophobic, fluorinated L-phenylalanine based spacer (Scheme 1). The metal chelate selected is a Gd(III)-DTPA complex that contains two overall remaining charges per complex and necessarily two counter ions. The one-dimensional growth of the aggregate is directed by &pi;-&pi; stacking and intermolecular hydrogen bonding. However, the electrostatic, repulsive forces that arise from the charges on the Gd(III)-DTPA complex start limiting the one-dimensional growth of the BTA-based discotic once a certain size is reached. At millimolar concentrations the formed aggregate has a spherical shape and a diameter of around 5 nm as inferred from 1H-NMR spectroscopy, small angle X-ray scattering, and cryogenic transmission electron microscopy (cryo-TEM). The strength of the electrostatic repulsive interactions between molecules can be reduced by increasing the salt concentration of the buffered solutions. This screening of the charges induces a transition from spherical aggregates into elongated rods with a length &gt; 25 nm. Cryo-TEM allows to visualise the changes in shape and size. In addition, CD spectroscopy permits to derive the mechanistic details of the self-assembly processes before and after the addition of salt. Importantly, the cooperativity -a key feature that dictates the physical properties of the produced supramolecular polymers- increases dramatically upon screening the electrostatic interactions. This increase in cooperativity results in a significant increase in the molecular weight of the formed supramolecular polymers in water.

The goal of this experiment is to determine and control the size, shape and stability of self-assembled discotic amphiphiles in water. For aqueous based supramolecular polymers such level of control is very difficult. We apply a strategy using both repulsive and attractive interactions. The experimental techniques applied to characterize this system are broadly applicable.

For aqueous based supramolecular polymers, the simultaneous control over shape, size and stability is very difficult1. At the same time, the ability to do ...

Fabrication of Spatially Confined Complex Oxides

Complex materials such as high Tc superconductors, multiferroics, and colossal magnetoresistors have electronic and magnetic properties that arise from the inherent strong electron correlations that reside within them. These materials can also possess electronic phase separation in which regions of vastly different resistive and magnetic behavior can coexist within a single crystal alloy material. By reducing the scale of these materials to length scales at and below the inherent size of the electronic domains, novel behaviors can be exposed. Because of this and the fact that spin-charge-lattice-orbital order parameters each involve correlation lengths, spatially reducing these materials for transport measurements is a critical step in understanding the fundamental physics that drives complex behaviors. These materials also offer great potential to become the next generation of electronic devices 1-3. Thus, the fabrication of low dimensional nano- or micro-structures is extremely important to achieve new functionality. This involves multiple controllable processes from high quality thin film growth to accurate electronic property characterization. Here, we present fabrication protocols of high quality microstructures for complex oxide manganite devices. Detailed descriptions and required equipment of thin film growth, photo-lithography, and wire-bonding are presented.

We describe the use of pulsed laser deposition (PLD), photolithography and wire-bonding techniques to create micrometer scale complex oxides devices. The PLD is utilized to grow epitaxial thin films. Photolithography and wire-bonding techniques are introduced to create practical devices for measurement purposes.

Complex materials such as high Tc superconductors, multiferroics, and colossal magnetoresistors have electronic and magnetic properties that arise from ...

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in industrialized countries. Here, the development of optical mesosensors/adsorbents (MSAs) for efficient recognition and selective recovery of Pd(II), Au(III), and Co(II) from urban mine was achieved. A simple, general method for preparing MSAs based on using high-order mesoporous monolithic scaffolds was described. Hierarchical cubic Ia3d wagon-wheel-shaped MSAs were fabricated by anchoring chelating agents (colorants) into three-dimensional pores and micrometric particle surfaces of the mesoporous monolithic scaffolds. Findings show, for the first time, evidence of controlled optical recognition of Pd(II), Au(III), and Co(II) ions and a highly selective system for recovery of Pd(II) ions (up to ~95%) in ores and industrial wastes. Furthermore, the controlled assessment processes described herein involve evaluation of intrinsic properties (e.g., visual signal change, long-term stability, adsorption efficiency, extraordinary sensitivity, selectivity, and reusability); thus, expensive, sophisticated instruments are not required. Results show evidence that MSAs will attract worldwide attention as a promising technological means of recovering and recycling palladium, gold and cobalt metals.

Because of the importance and extensive use of palladium, gold and cobalt metals in high-technology equipment, their recovery and recycling constitute an important industrial challenge. The metal recovery system described herein is a simple, low-cost means for the effective detection, removal, and recovery of these metals from the urban mine.

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in ...

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

The setup of a planar Frequency Mixing Magnetic Detection (p-FMMD) scanner for performing Magnetic Particles Imaging (MPI) of flat samples is presented. It consists of two magnetic measurement heads on both sides of the sample mounted on the legs of a u-shaped support. The sample is locally exposed to a magnetic excitation field consisting of two distinct frequencies, a stronger component at about 77 kHz and a weaker field at 61 Hz. The nonlinear magnetization characteristics of superparamagnetic particles give rise to the generation of intermodulation products. A selected sum-frequency component of the high and low frequency magnetic field incident on the magnetically nonlinear particles is recorded by a demodulation electronics. In contrast to a conventional MPI scanner, p-FMMD does not require the application of a strong magnetic field to the whole sample because mixing of the two frequencies occurs locally. Thus, the lateral dimensions of the sample are just limited by the scanning range and the supports. However, the sample height determines the spatial resolution. In the current setup it is limited to 2 mm. As examples, we present two 20 mm &#215; 25 mm p-FMMD images acquired from samples with 1 &#181;m diameter maghemite particles in silanol matrix and with 50 nm magnetite particles in aminosilane matrix. The results show that the novel MPI scanner can be applied for analysis of thin biological samples and for medical diagnostic purposes.

A scanner for imaging magnetic particles in planar samples was developed using the planar frequency mixing magnetic detection technique. The magnetic intermodulation product response from the nonlinear nonhysteretic magnetization of the particles is recorded upon a two-frequency excitation. It can be used to take 2D images of thin biological samples.

The setup of a planar Frequency Mixing Magnetic Detection (p-FMMD) scanner for performing Magnetic Particles Imaging (MPI) of flat samples is presented. ...

A High Performance Impedance-based Platform for Evaporation Rate Detection

This paper describes the method of a novel impedance-based platform for the detection of the evaporation rate. The model compound hyaluronic acid was employed here for demonstration purposes. Multiple evaporation tests on the model compound as a humectant with various concentrations in solutions were conducted for comparison purposes. A conventional weight loss approach is known as the most straightforward, but time-consuming, measurement technique for evaporation rate detection. Yet, a clear disadvantage is that a large volume of sample is required and multiple sample tests cannot be conducted at the same time. For the first time in literature, an electrical impedance sensing chip is successfully applied to a real-time evaporation investigation in a time sharing, continuous and automatic manner. Moreover, as little as 0.5 ml of test samples is required in this impedance-based apparatus, and a large impedance variation is demonstrated among various dilute solutions. The proposed high-sensitivity and fast-response impedance sensing system is found to outperform a conventional weight loss approach in terms of evaporation rate detection.

This paper presents an impedance-based apparatus for evaporation rate detection of solutions. It offers clear advantages over a conventional weight loss approach: a fast response, high-sensitivity detection, a small sample requirement, multiple sample measurements, and easy disassembly for cleaning and reuse purposes.

This paper describes the method of a novel impedance-based platform for the detection of the evaporation rate. The model compound hyaluronic acid was ...

Dielectric RheoSANS &#8212; Simultaneous Interrogation of Impedance, Rheology and Small Angle Neutron Scattering of Complex Fluids

A procedure for the operation of a new dielectric RheoSANS instrument capable of simultaneous interrogation of the electrical, mechanical and microstructural properties of complex fluids is presented. The instrument consists of a Couette geometry contained within a modified forced convection oven mounted on a commercial rheometer. This instrument is available for use on the small angle neutron scattering (SANS) beamlines at the National Institute of Standards and Technology (NIST) Center for Neutron Research (NCNR). The Couette geometry is machined to be transparent to neutrons and provides for measurement of the electrical properties and microstructural properties of a sample confined between titanium cylinders while the sample undergoes arbitrary deformation. Synchronization of these measurements is enabled through the use of a customizable program that monitors and controls the execution of predetermined experimental protocols. Described here is a protocol to perform a flow sweep experiment where the shear rate is logarithmically stepped from a maximum value to a minimum value holding at each step for a specified period of time while frequency dependent dielectric measurements are made. Representative results are shown from a sample consisting of a gel composed of carbon black aggregates dispersed in propylene carbonate. As the gel undergoes steady shear, the carbon black network is mechanically deformed, which causes an initial decrease in conductivity associated with the breaking of bonds comprising the carbon black network. However, at higher shear rates, the conductivity recovers associated with the onset of shear thickening. Overall, these results demonstrate the utility of the simultaneous measurement of the rheo-electro-microstructural properties of these suspensions using the dielectric RheoSANS geometry.

Here, we present a procedure for the measurement of simultaneous impedance, rheology and neutron scattering from soft matter materials under shear flow.

A procedure for the operation of a new dielectric RheoSANS instrument capable of simultaneous interrogation of the electrical, mechanical and ...

Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light

A strategy based on doped liquid crystalline networks is described to create mechanical self-sustained oscillations of plastic films under continuous light irradiation. The photo-excitation of dopants that can quickly dissipate light into heat, coupled with anisotropic thermal expansion and self-shadowing of the film, gives rise to the self-sustained deformation. The oscillations observed are influenced by the dimensions and the modulus of the film, and by the directionality and intensity of the light. The system developed offers applications in energy conversion and harvesting for soft-robotics and automated systems.
The general method described here consists of creating free-standing liquid crystalline films and characterizing the mechanical and thermal effects observed. The molecular alignment is achieved using alignment layers (rubbed polyimide), commonly used in the display manufacturing industry. To obtain actuators with large deformation, the mesogens are aligned and polymerized in a splay/bend configuration, i.e., with the director of the liquid crystals (LCs) going gradually from planar to homeotropic through the film thickness. Upon irradiation, the mechanical and thermal oscillations obtained are monitored with a high-speed camera. The results are further quantified by image analysis using an image processing program.

The goal of the protocol is to create liquid crystalline polymer films that can mechanically oscillate under continuous light irradiation. We describe in great detail the conception of free-standing films, from the liquid crystal alignment method to the photo-actuation. The experimental protocol applied to prepare this material is broadly applicable.

A strategy based on doped liquid crystalline networks is described to create mechanical self-sustained oscillations of plastic films under continuous ...

A Pipette-Tip Based Method for Seeding Cells to Droplet Microfluidic Platforms

Amongst various microfluidic platform designs frequently used for cellular analysis, droplet-microfluidics provides a robust tool for isolating and analyzing cells at the single-cell level by eliminating the influence of external factors on the cellular microenvironment. Encapsulation of cells in droplets is dictated by the Poisson distribution as a function of the number of cells present in each droplet and the average number of cells per volume of droplet. Primary cells, especially immune cells, or clinical specimens can be scarce and loss-less encapsulation of cells remains challenging. In this paper, we present a new methodology that uses pipette-tips to load cells to droplet-based microfluidic devices without the significant loss of cells. With various cell types , we demonstrate efficient cell encapsulation in droplets that closely corresponds to the encapsulation efficiency predicted by the Poisson distribution. Our method ensures loss-less loading of cells to microfluidic platforms and can be easily adapted for downstream single cell analysis, e.g., to decode cellular interactions between different cell types.

This article presents a protocol for seeding scarce population of cells using pipette-tips to droplet microfluidic devices in order to provide higher encapsulation efficiency of cells in droplets.

Amongst various microfluidic platform designs frequently used for cellular analysis, droplet-microfluidics provides a robust tool for isolating and ...

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 ...

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

We present a protocol for performing gas spectroscopy using infrared degenerated four-wave mixing (IR-DFWM), for the quantitative detection of gas species in the ppm-to-single-percent range. The main purpose of the method is the spatially resolved detection of low-concentration species, which have no transitions in the visible or near-IR spectral range that could be used for detection. IR-DFWM is a nonintrusive method, which is a great advantage in combustion research, as inserting a probe into a flame can change it drastically. The IR-DFWM is combined with upconversion detection. This detection scheme uses sum-frequency generation to move the IR-DFWM signal from the mid-IR to the near-IR region, to take advantage of the superior noise characteristics of silicon-based detectors. This process also rejects most of the thermal background radiation. The focus of the protocol presented here is on the proper alignment of the IR-DFWM optics and on how to align an intracavity upconversion detection system.

Here, we present a protocol to perform sensitive, spatially resolved gas spectroscopy in the mid-infrared region, using degenerate four-wave mixing combined with upconversion detection.

We present a protocol for performing gas spectroscopy using infrared degenerated four-wave mixing (IR-DFWM), for the quantitative detection of gas species ...