The authors would like to thank Tamara Bucher from the University of Newcastle, Australia, for providing the fake-food image dataset. This work was supported by the European Union&#39;s Horizon 2020 research and innovation programs (grant numbers 863059 - FNS-Cloud, 769661 - SAAM); and the Slovenian Research Agency (grant number P2-0098). The European Union and Slovenian Research Agency had no role in the design, analysis or writing of this article.

1. Food image recognition with NutriNet
<ol>
	<li>Obtaining the food image dataset
	<ol>
		<li>Gather a list of different foods and beverages that will be the outputs of the food image recognition model. A varied list of popular foods and beverages is preferred, as that will allow the training of a robust food image recognition model.</li>
		<li>Save the food and beverage list in a text file (e.g., &#39;txt&#39; or &#39;csv&#39;). 
		NOTE: The text file used by the authors of this article can be found in the supplemental files (&#39;food_items.txt&#39;) and includes a list of 520 Slovenian food items.</li>
		<li>Write or download a Python43 script that uses the Google Custom Search API44 to download images of each food item from the list and saves them into a separate folder for each food item. 
		&#8203;NOTE: The Python script used by the authors of this article can be found in the supplemental files (&#39;download_images.py&#39;). If this script is used, the Developer Key (variable &#39;developerKey&#39;, line 8 in the Python script code) and Custom Search Engine ID (variable &#39;cx&#39;, line 28 in the Python script code) need to be replaced with values specific to the Google account being used.</li>
		<li>Run the Python script from step 1.1.3 (e.g., with the command: &#39;python download_images.py&#39;).</li>
	</ol>
	</li>
	<li>(Optional) Cleaning the food image dataset
	<ol>
		<li>Train a food image detection model in the same way as in section 1.4, except use only two outputs (food, non-food) as opposed to the list of outputs from step 1.1.1. 
		NOTE: The authors of this article used images combined from recipe websites and the ImageNet dataset45 to train the food image detection model. Since the focus here is on food image recognition and this is an optional step for cleaning the recognition dataset, further details are omitted. Instead, more details about this approach can be found in Mezgec et al.2.</li>
		<li>Run the detection model from step 1.2.1 on the food image dataset that is the result of step 1.1.4.</li>
		<li>Delete every image that was tagged as non-food by the detection model from step 1.2.1.</li>
		<li>Manually check the food image dataset for other erroneous or low-quality images, and for image duplicates.</li>
		<li>Delete images found in step 1.2.4.</li>
	</ol>
	</li>
	<li>Augmenting the food image dataset
	<ol>
		<li>Create a new version of each image from the food image dataset by rotating it by 90&#176; using the CLoDSA library46 (lines 19 to 21 in the included Python script). 
		NOTE: The Python script containing all the CLoDSA commands used by the authors of this article can be found in a file included in the supplemental files (&#39;nutrinet_augmentation.py&#39;). If this script is used, the Input Path (variable &#39;INPUT_PATH&#39;, line 8 in the Python script code) and Output Path (variable &#39;OUTPUT_PATH&#39;, line 11 in the Python script code) need to be replaced with paths to the desired folders.</li>
		<li>Create a new version of each image from the food image dataset by rotating it by 180&#176; using the CLoDSA library (lines 19 to 21 in the included Python script).</li>
		<li>Create a new version of each image from the food image dataset by rotating it by 270&#176; using the CLoDSA library (lines 19 to 21 in the included Python script).</li>
		<li>Create a new version of each image from the food image dataset by flipping it horizontally using the CLoDSA library (lines 23 and 24 in the included Python script).</li>
		<li>Create a new version of each image from the food image dataset by adding random color noise to it using the CLoDSA library (lines 26 and 27 in the included Python script).</li>
		<li>Create a new version of each image from the food image dataset by zooming into it by 25% using the CLoDSA library (lines 29 and 30 in the included Python script).</li>
		<li>Save images from steps 1.3.1-1.3.6, along with the original images (lines 16 and 17 in the included Python script), into a new food image dataset (in total, 7 variants per food image). This is done by executing the command in line 32 of the included Python script.</li>
	</ol>
	</li>
	<li>Performing food image recognition
	<ol>
		<li>Import the food image dataset from step 1.3.7 into the NVIDIA DIGITS environment47, dividing the dataset into training, validation and testing subsets in the NVIDIA DIGITS user interface.</li>
		<li>Copy and paste the definition text of the NutriNet architecture2 into NVIDIA DIGITS as a custom network. 
		NOTE: The NutriNet architecture definition text can be found in the supplemental files (&#39;nutrinet.prototxt&#39;).</li>
		<li>(Optional) Define training hyperparameters in the NVIDIA DIGITS user interface. 
		NOTE: Hyperparameters are parameters that are used to define the training process prior to its start. The hyperparameters used by the authors of this article can be found in a file included in the supplemental files (&#39;nutrinet_hyperparameters.prototxt&#39;). While experimentation is needed for each dataset to find the optimal hyperparameters, the file contains a hyperparameter configuration which can be copied into the NVIDIA DIGITS user interface. Furthermore, NVIDIA DIGITS populates the hyperparameters with default values which can be used as a baseline. This step is therefore optional.</li>
		<li>Run the training of the NutriNet model.</li>
		<li>After training is complete, take the best-performing NutriNet model iteration. This model is then used for testing the performance of this approach. 
		&#8203;NOTE: There are multiple ways to determine the best-performing model iteration. A straightforward way to do this is as follows. NVIDIA DIGITS outputs a graph of accuracy measures for each training epoch. Check which epoch achieved the lowest loss value for the validation subset of the food image dataset - that model iteration can be considered best-performing. An optional step in determining the best-performing model iteration is to observe how the loss value for the training subset changes from epoch to epoch and if it starts to drop continuously while the loss value for the validation subset remains the same or rises continuously, take the epoch prior to this drop in training loss value, as that can signal when the model started overfitting on the training images.</li>
	</ol>
	</li>
</ol>
2. Food image segmentation with FCNs
<ol>
	<li>Obtaining the fake-food image dataset
	<ol>
		<li>Obtain a dataset of fake-food images. Fake-food images are gathered by researchers conducting behavioral studies using food replicas. 
		NOTE: The authors of this article received images of fake food that were collected in a lab environment18.</li>
		<li>Manually annotate every food image on a pixel level - each pixel in the image must contain information about which food class it belongs to. The result of this step is one annotation image for each image from the food image dataset, where each pixel represents one of the food classes. 
		&#8203;NOTE: There are many tools to achieve this - the authors of this article used JavaScript Segment Annotator48.</li>
	</ol>
	</li>
	<li>Augmenting the fake-food image dataset
	<ol>
		<li>Perform the same steps as in section 1.3, but only on images from the training subset of the food image dataset. 
		NOTE: With the exception of step 1.3.5, all data augmentation steps need to be performed on corresponding annotation images as well. If the script from section 1.3 is used, the Input Path (variable &#39;INPUT_PATH&#39;, line 8 in the Python43 script code) and Output Path (variable &#39;OUTPUT_PATH&#39;, line 11 in the Python script code) need to be replaced with paths to the desired folders. In addition, set the Problem (variable &#39;PROBLEM&#39;, line 6 in the Python script code) to &#39;instance_segmentation&#39; and the Annotation Mode (variable &#39;ANNOTATION_MODE&#39;, line 7 in the Python script code) and Output Mode (variable &#39;OUTPUT_MODE&#39;, line 10 in the Python script code) to &#39;coco&#39;.</li>
	</ol>
	</li>
	<li>Performing fake-food image segmentation
	<ol>
		<li>Perform the same steps as in section 1.4, with the exception of step 1.4.2. In place of that step, perform steps 2.3.2 and 2.3.3. 
		NOTE: Hyperparameters are parameters that are used to define the training process prior to its start. The training hyperparameters used by the authors of this article for the optional step 1.4.3 can be found in a file included in the supplemental files (&#39;fcn-8s_hyperparameters.prototxt&#39;). While experimentation is needed for each dataset to find the optimal set of hyperparameters, the file contains a hyperparameter configuration which can be copied into the NVIDIA DIGITS47 user interface. Furthermore, NVIDIA DIGITS populates the hyperparameters with default values which can be used as a baseline.</li>
		<li>Copy and paste the definition text of the FCN-8s architecture15 into the NVIDIA DIGITS environment as a custom network. 
		NOTE: The FCN-8s architecture definition text is publicly available on GitHub49.</li>
		<li>Enter the path to the pre-trained FCN-8s model weights into the NVIDIA DIGITS user interface. 
		&#8203;NOTE: These model weights were pre-trained on the PASCAL VOC dataset17 and can be found on the Internet49.</li>
	</ol>
	</li>
</ol>
3. Food image segmentation with HTC ResNet
<ol>
	<li>Obtaining the food image dataset
	<ol>
		<li>Download the food image dataset from the FRC website19.</li>
	</ol>
	</li>
	<li>Augmenting the food image dataset
	<ol>
		<li>Perform steps 1.3.1-1.3.4. 
		NOTE: The Python43 script containing all the CLoDSA46 commands used by the authors of this article can be found in a file included in the supplemental files (&#39;frc_augmentation.py&#39;). If this script is used, the Input Path (variable &#39;INPUT_PATH&#39;, line 8 in the Python script code) and Output Path (variable &#39;OUTPUT_PATH&#39;, line 11 in the Python script code) need to be replaced with paths to the desired folders.</li>
		<li>Create a new version of each image from the food image dataset by adding Gaussian blur to it using the CLoDSA library (lines 26 and 27 in the included Python script).</li>
		<li>Create a new version of each image from the food image dataset by sharpening it using the CLoDSA library (lines 29 and 30 in the included Python script).</li>
		<li>Create a new version of each image from the food image dataset by applying gamma correction to it using the CLoDSA library (lines 32 and 33 in the included Python script).</li>
		<li>Save images from steps 3.2.1-3.2.4, along with the original images (lines 16 and 17 in the included Python script), into a new food image dataset (in total, 8 variants per food image). This is done by executing the command in line 35 of the included Python script.</li>
		<li>Save images from steps 3.2.2-3.2.4, along with the original images (lines 16 and 17 in the included Python script), into a new food image dataset (in total, 4 variants per food image). This is done by deleting lines 19 to 24 of the included Python script and executing the command in line 35.</li>
	</ol>
	</li>
	<li>Performing food image segmentation
	<ol>
		<li>Modify the existing HTC20 ResNet-101 architecture16 definition from the MMDetection library50 in sections &#39;model settings&#39; and &#39;dataset settings&#39; of the architecture definition file so that it accepts the food image datasets from steps 3.1.1, 3.2.5 and 3.2.6.</li>
		<li>(Optional) Modify the HTC ResNet-101 architecture definition from step 3.3.1 to define training hyperparameters: batch size in section &#39;dataset settings&#39;, solver type and learning rate in section &#39;optimizer&#39;, learning policy in section &#39;learning policy&#39; and number of training epochs in section &#39;runtime settings&#39; of the architecture definition file. 
		&#8203;NOTE: The modified HTC ResNet-101 architecture definition file can be found in the supplemental files (&#39;htc_resnet-101.py&#39;). Hyperparameters are parameters that are used to define the training process prior to its start. While experimentation is needed for each dataset to find the optimal set of hyperparameters, the file already contains a hyperparameter configuration which can be used without modification. This step is therefore optional.</li>
		<li>Run the training of the HTC ResNet-101 model on the food image dataset from step 3.1.1 using the MMDetection library (e.g., with the command: &#39;python mmdetection/tools/train.py htc_resnet-101.py&#39;).</li>
		<li>After the training from step 3.3.3 is complete, take the best-performing HTC ResNet-101 model iteration and fine-tune it by running the next phase of training on the food image dataset from step 3.2.5. 
		&#8203;NOTE: There are multiple ways to determine the best-performing model iteration. A straightforward way to do this is as follows. The MMDetection library outputs values of accuracy measures for each training epoch in the command line interface. Check which epoch achieved the lowest loss value for the validation subset of the food image dataset - that model iteration can be considered best-performing. An optional step in determining the best-performing model iteration is to observe how the loss value for the training subset changes from epoch to epoch and if it starts to drop continuously while the loss value for the validation subset remains the same or rises continuously, take the epoch prior to this drop in training loss value, as that can signal when the model started overfitting on the training images.</li>
		<li>After the training from step 3.3.4 is complete, take the best-performing HTC ResNet-101 model iteration and fine-tune it by running the next phase of training on the food image dataset from step 3.2.6. 
		&#8203;NOTE: See note for step 3.3.4.</li>
		<li>After the training from step 3.3.5 is complete, take the best-performing HTC ResNet-101 model iteration and fine-tune it by again running the next phase of training on the food image dataset from step 3.2.5. 
		&#8203;NOTE: See note for step 3.3.4.</li>
		<li>After the training from step 3.3.6 is complete, take the best-performing HTC ResNet-101 model iteration. This model is then used for testing the performance of this approach. 
		NOTE: See note for step 3.3.4. Steps 3.3.3-3.3.7 yielded the best results for the purposes defined by the authors of this article. Experimentation is needed for each dataset to find the optimal sequence of training and data augmentation steps.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

In recent years, deep neural networks have been validated multiple times as a suitable solution for recognizing food images10,11,12,21,23,25,26,29,31,33. Our work presented in this article serves to further prove this1,2. The single-output food image recognition approach is straightforward and can be used for simple applications where images with only one food or beverage item are expected2.
The food image segmentation approach seems particularly suitable for recognizing food images in general, without any restriction on the number of food items1. Because it works by classifying each individual pixel of the image, it is able to not only recognize any number of food items in the image, but also specify where a food item is located, as well as how large it is. The latter can then be used to perform food weight estimation, particularly if used with either a reference object or a fixed-distance camera.
There has been some work done regarding the availability of food image datasets3,22,27,30,36,37,38,39,40,41,42, and we hope more will be done in the future, particularly when it comes to aggregating food image datasets from different regions across the world, which would enable more robust solutions to be developed. Currently, the accuracy of automatic food image recognition solutions has not yet reached human-level accuracy35, and this is likely in large part due to the usage of food image datasets of insufficient size and quality.
In the future, our goal will be to further evaluate the developed procedures on real-world images. In general, datasets in this field often contain images taken in controlled environments or images that were manually optimized for recognition. This is why it is important to gather a large and diverse real-world food image dataset to encompass all the different food and beverage items that individuals might want to recognize. The first step towards this was provided by the Food Recognition Challenge, which included a dataset of real-world food images19, but further work needs to be done to validate this approach on food images from all around the world and in cooperation with dietitians.

Dietary assessment is a crucial step in determining actionable areas of an individual&#39;s diet. However, performing dietary assessment using traditionally manual approaches is associated with considerable costs. These approaches are also prone to errors as they often rely on self-reporting by the individual. Automated dietary assessment addresses these issues by providing a simpler way to quantify and qualify food intake. Such an approach can also alleviate some of the errors present in manual approaches, such as missed meals, inability to accurately assess food volume, etc. Therefore, there are clear benefits to automating dietary assessment by developing solutions that identify different foods and beverages and quantify food intake1. These solutions can also be used to enable an estimation of nutritional values of food and beverage items (henceforth &#39;food items&#39;). Consequently, automated dietary assessment is useful for multiple applications - from strictly medical uses, such as allowing dietitians to more easily and accurately track and analyze their patients&#39; diets, to the usage inside well-being apps targeted at the general population.
Automatically recognizing food items from images is a challenging computer vision problem. This is due to foods being typically deformable objects, and due to the fact that a large amount of the food item&#39;s visual information can be lost during its preparation. Additionally, different foods can appear to be very similar to each other, and the same food can appear to be substantially different on multiple images2. Furthermore, the recognition accuracy depends on many more factors, such as image quality, whether the food item is obstructed by another item, distance from which the image was taken, etc. Recognizing beverage items presents its own set of challenges, the main one being the limited amount of visual information that is available in an image. This information could be the beverage color, beverage container color and structure, and, under optimal image conditions, the beverage density2.
To successfully recognize food items from images, it is necessary to learn features of each food and beverage class. This was traditionally done using manually-defined feature extractors3,4,5,6 that perform recognition based on specific item features like color, texture, size, etc., or a combination of these features. Examples of these feature extractors include multiple kernel learning4, pairwise local features5 and the bag-of-features model6. Due to the complexity of food images, these approaches mostly achieved a low classification accuracy - between 10% and 40%3,4,5. The reason for this is that the manual approach is not robust enough to be sufficiently accurate. Because a food item can vary significantly in appearance, it is not feasible to encompass all these variances manually. Higher classification accuracy can be achieved with manually-defined feature extractors when either the number of food classes is reduced5, or different image features are combined6, thus indicating that there is a need for more complex solutions to this problem.
This is why deep learning proved to be so effective for the food image recognition problem. Deep learning, or deep neural networks, was inspired by biological brains, and allows computational models composed of multiple processing layers to automatically learn features through training on a set of input images7,8. Because of this, deep learning has substantially improved the state of the art in a variety of research fields7, with computer vision, and subsequently food image recognition, being one of them2.
In particular, deep convolutional neural networks (DCNNs) are most popular for food image recognition - these networks are inspired by the visual system of animals, where individual neurons try to gain an understanding of the visual input by reacting to overlapping regions in the visual field9. A convolutional neural network takes the input image and performs a series of operations in each of the network layers, the most common of which are convolutional, fully-connected and pooling layers. Convolutional layers contain learnable filters that respond to certain features in the input data, whereas fully-connected layers compose output data from other layers to gain higher-level knowledge from it. The goal of pooling layers is to down-sample the input data2. There are two approaches to using deep learning models that proved popular: taking an existing deep neural network definition10,11, referred to as a deep learning architecture in this article, or defining a new deep learning architecture12,13, and training either one of these on a food image dataset. There are strengths and weaknesses to both approaches - when using an existing deep learning architecture, an architecture that performed well for other problems can be chosen and fine-tuned for the desired problem, thus saving time and ensuring that a validated architecture has been chosen. Defining a new deep learning architecture, on the other hand, is more time-intensive, but allows the development of architectures that are specifically made to take into account the specifics of a problem and thus theoretically perform better for that problem.
In this article, we present both approaches. For the food image recognition problem, we developed a novel DCNN architecture called NutriNet2, which is a modification of the well-known AlexNet architecture14. There are two main differences compared to AlexNet: NutriNet accepts 512x512-pixel images as input (as opposed to 256x256-pixel images for AlexNet), and NutriNet has an additional convolutional layer at the beginning of the neural network. These two changes were introduced in order to extract as much information from the recognition dataset images as possible. Having higher-resolution images meant that there is more information present on images and having more convolutional layers meant that additional knowledge could be extracted from the images. Compared to AlexNet&#39;s around 60 million parameters, NutriNet contains less parameters: approximately 33 million. This is because of the difference in dimensionality at the first fully-connected layer caused by the additional convolutional layer2. Figure 1 contains a diagram of the NutriNet architecture. The food images that were used to train the NutriNet model were gathered from the Internet - the procedure is described in the protocol text.
For the food image segmentation problem, we used two different existing architectures: fully convolutional networks (FCNs)15 and deep residual networks (ResNet)16, both of which represented the state of the art for image segmentation when we used them to develop their respective food image segmentation solutions. There are multiple FCN variants that were introduced by Long et al.: FCN-32s, FCN-16s and FCN-8s15. FCN-32s outputs a pixel map based on the predictions by the FCN&#39;s final layer, whereas the FCN-16s variant combines these predictions with those by an earlier layer. FCN-8s considers yet another layer&#39;s predictions and is therefore able to make predictions at the finest grain, which is why it is suitable for food image recognition. The FCN-8s that we used was pre-trained on the PASCAL Visual Object Classes (PASCAL VOC) dataset17 and trained and tested on images of food replicas (henceforth &#39;fake food&#39;)18 due to their visual resemblance to real food and due to a lack of annotated images of real food on a pixel level. Fake food is used in different behavioral studies and images are taken for all dishes from all study participants. Because the food contents of these images are known, it makes the image dataset useful for deep learning model training. Dataset processing steps are described in the protocol text.
The ResNet-based solution was developed in the scope of the Food Recognition Challenge (FRC)19. It uses the Hybrid Task Cascade (HTC)20 method with a ResNet-10116 backbone. This is a state-of-the-art approach for the image segmentation problem that can use different feature extractors, or backbones. We considered other backbone networks as well, particularly other ResNet variants such as ResNet-5016, but ResNet-101 was the most suitable due to its depth and ability to represent input images in a complex enough manner. The dataset used for training the HTC ResNet-101 model was the FRC dataset with added augmented images. These augmentations are presented in the protocol text.
This article is intended as a resource for machine learning experts looking for information about which deep learning architectures and data augmentation steps perform well for the problems of food image recognition and segmentation, as well as for nutrition researchers looking to use our approach to automate food image recognition for use in dietary assessment. In the paragraphs below, deep learning solutions and datasets from the food image recognition field are presented. In the protocol text, we detail how each of the three approaches was used to train deep neural network models that can be used for automated dietary assessment. Additionally, each protocol section contains a description of how the food image datasets used for training and testing were acquired and processed.
DCNNs generally achieved substantially better results than other methods for food image recognition and segmentation, which is why the vast majority of recent research in the field is based on these networks. Kawano et al. used DCNNs to complement manual approaches21 and achieved a classification accuracy of 72.26% on the UEC-FOOD100 dataset22. Christodoulidis et al. used them exclusively to achieve a higher accuracy of 84.90% on a self-acquired dataset23. Tanno et al. developed DeepFoodCam - a smartphone app for food image recognition that uses DCNNs24. Liu et al. presented a system that performs an Internet of Things-based dietary assessment using DCNNs25. Martinel et al. introduced a DCNN-based approach that exploits the specifics of food images26 and reported an accuracy of 90.27% on the Food-101 dataset27. Zhou et al. authored a review of deep learning solutions in the food domain28.
Recently, Zhao et al. proposed a network specifically for food image recognition in mobile applications29. This approach uses a smaller &#39;student&#39; network that learns from a larger &#39;teacher&#39; network. With it, they managed to achieve an accuracy of 84% on the UEC-FOOD25630 and an accuracy of 91.2% on the Food-101 dataset27. Hafiz et al. used DCNNs to develop a beverage-only image recognition solution and reported a very high accuracy of 98.51%31. Shimoda et al. described a novel method for detecting plate regions in food images without the usage of pixel-wise annotation32. Ciocca et al. introduced a new dataset containing food items from 20 different food classes in 11 different states (solid, sliced, creamy paste, etc.) and presented their approach for training recognition models that are able to recognize the food state, in addition to the food class33. Knez et al. evaluated food image recognition solutions for mobile devices34. Finally, Furtado et al. conducted a study on how the human visual system compares to the performance of DCNNs and found that human recognition still outperforms DCNNs with an accuracy of 80% versus 74.5%35. The authors noted that with a small number of food classes, the DCNNs perform well, but on a dataset with hundreds of classes, human recognition accuracy is higher35, highlighting the complexity of the problem.
Despite its state-of-the-art results, deep learning has a major drawback - it requires a large input dataset to train the model on. In the case of food image recognition, a large food image dataset is required, and this dataset needs to encompass as many different real-world scenarios as possible. In practice this means that for each individual food or beverage item, a large collection of images is required, and as many different items as possible need to be present in the dataset. If there are not enough images for a specific item in the dataset, that item is unlikely to be recognized successfully. On the other hand, if only a small number of items is covered by the dataset, the solution will be limited in scope, and only able to recognize a handful of different foods and beverages.
Multiple datasets were made available in the past. The Pittsburgh Fast-Food Image Dataset (PFID)3 was introduced to encourage more research in the field of food image recognition. The University of Electro-Communications Food 100 (UEC-FOOD100)22 and University of Electro-Communications Food 256 (UEC-FOOD256)30 datasets contain Japanese dishes, expanded with some international dishes in the case of the UEC-FOOD256 dataset. The Food-101 dataset contains popular dishes acquired from a website27. The Food-5036 and Video Retrieval Group Food 172 (VireoFood-172)37 datasets are Chinese-based collections of food images. The University of Milano-Bicocca 2016 (UNIMIB2016) dataset is composed of images of food trays from an Italian canteen38. Recipe1M is a large-scale dataset of cooking recipes and food images39. The Food-475 dataset40 collects four previously published food image datasets27,30,36,37 into one. The Beijing Technology and Business University Food 60 (BTBUFood-60) is a dataset of images meant for food detection41. Recently, the ISIA Food-500 dataset42 of miscellaneous food images was made available. In comparison to other publicly available food image datasets, it contains a large number of images, divided into 500 food classes, and is meant to advance the development of multimedia food recognition solutions42.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>HARDWARE</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>NVIDIA GPU</td>
			<td>NVIDIA</td>
			<td>N/A</td>
			<td>An NVIDIA GPU is needed as some of the software frameworks below will not work otherwise. https://www.nvidia.com</td>
		</tr>
		<tr>
			<td>SOFTWARE</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Caffe</td>
			<td>Berkeley AI Research</td>
			<td>N/A</td>
			<td>Caffe is a deep learning framework. https://caffe.berkeleyvision.org</td>
		</tr>
		<tr>
			<td>CLoDSA</td>
			<td>J&#243;nathan Heras</td>
			<td>N/A</td>
			<td>CLoDSA is a Python image augmentation library. https://github.com/joheras/CLoDSA</td>
		</tr>
		<tr>
			<td>Google API Client</td>
			<td>Google</td>
			<td>N/A</td>
			<td>Google API Client is a Python client library for Google&#39;s discovery based APIs. https://github.com/googleapis/google-api-python-client</td>
		</tr>
		<tr>
			<td>JavaScript Segment Annotator</td>
			<td>Kota Yamaguchi</td>
			<td>N/A</td>
			<td>JavaScript Segment Annotator is a JavaScript image annotation tool. https://github.com/kyamagu/js-segment-annotator</td>
		</tr>
		<tr>
			<td>MMDetection</td>
			<td>Multimedia Laboratory, CUHK</td>
			<td>N/A</td>
			<td>MMDetection is an object detection toolbox based on PyTorch. https://github.com/open-mmlab/mmdetection</td>
		</tr>
		<tr>
			<td>NVIDIA DIGITS</td>
			<td>NVIDIA</td>
			<td>N/A</td>
			<td>NVIDIA DIGITS is a wrapper for Caffe that provides a graphical web interface. https://developer.nvidia.com/digits</td>
		</tr>
		<tr>
			<td>OpenCV</td>
			<td>Intel</td>
			<td>N/A</td>
			<td>OpenCV is a library for computer vision. https://opencv.org</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>Python Software Foundation</td>
			<td>N/A</td>
			<td>Python is a programming language. https://www.python.org</td>
		</tr>
		<tr>
			<td>PyTorch</td>
			<td>Facebook AI Research</td>
			<td>N/A</td>
			<td>PyTorch is a machine learning framework. https://pytorch.org</td>
		</tr>
		<tr>
			<td>Ubuntu OS</td>
			<td>Canonical</td>
			<td>N/A</td>
			<td>Ubuntu 14.04 is the OS used by the authors and offers compatibility with all of the software frameworks and tools above. https://ubuntu.com</td>
		</tr>
	</tbody>
</table>

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).

NutriNet was tested against three popular deep learning architectures of the time: AlexNet14, GoogLeNet51 and ResNet16. Multiple training parameters were also tested for all architectures to define the optimal values2. Among these is the choice of solver type, which determines how the loss function is minimized. This function is the primary quality measure for training neural networks as it is better suited for optimization during training than classification accuracy. We tested three solvers: Stochastic Gradient Descent (SGD)52, Nesterov&#39;s Accelerated Gradient (NAG)53 and the Adaptive Gradient algorithm (AdaGrad)54. The second parameter is batch size, which defines the number of images that are processed at the same time. The depth of the deep learning architecture determined the value of this parameter, as deeper architectures require more space in the GPU memory - the consequence of this approach was that the memory was completely filled with images for all architectures, regardless of depth. The third parameter is learning rate, which defines the speed with which the neural network parameters are being changed during training. This parameter was set in unison with the batch size, as the number of concurrently processed images dictates the convergence rate. AlexNet models were trained using a batch size of 256 images and a base learning rate of 0.02; NutriNet used a batch size of 128 images and a rate of 0.01; GoogLeNet 64 images and a rate of 0.005; and ResNet 16 images and a rate of 0.00125. Three other parameters were fixed for all architectures: learning rate policy (step-down), step size (30%) and gamma (0.1). These parameters jointly describe how the learning rate is changing in every epoch. The idea behind this approach is that the learning rate is being gradually lowered to fine-tune the model the closer it gets to the optimal loss value. Finally, the number of training epochs was also fixed to 150 for all deep learning architectures2.
The best result among all the parameters tested that NutriNet achieved was a classification accuracy of 86.72% on the recognition dataset, which was around 2% higher than the best result for AlexNet and slightly higher than GoogLeNet&#39;s best result. The best-performing architecture overall was ResNet (by around 1%), however the training time for ResNet is substantially higher compared to NutriNet (by a factor of approximately five), which is important if models are continuously re-trained to improve accuracy and the number of recognizable food items. NutriNet, AlexNet and GoogLeNet achieved their best results using the AdaGrad solver, whereas ResNet&#39;s best model used the NAG solver. NutriNet was also tested on the publicly available UNIMIB2016 food image dataset38. This dataset contains 3,616 images of 73 different food items. NutriNet achieved a recognition accuracy of 86.39% on this dataset, slightly outperforming the baseline recognition result of the authors of the dataset, which was 85.80%. Additionally, NutriNet was tested on a small dataset of 200 real-world images of 115 different food and beverage items, where NutriNet achieved a top-5 accuracy of 55%.
To train the FCN-8s fake-food image segmentation model, we used Adam55 as the solver type, as we found that it performed optimally for this task. The base learning rate was set very low - to 0.0001. The reason for the low number is the fact that only one image could be processed at a time, which is a consequence of the pixel-level classification process. The GPU memory requirements for this approach are significantly greater than image-level classification. The learning rate thus had to be set low so that the parameters were not being changed too fast and converge to less optimal values. The number of training epochs was set to 100, while the learning rate policy, step size and gamma were set to step-down, 34% and 0.1, respectively, as these parameters produced the most accurate models.
Accuracy measurements of the FCN-8s model were performed using the pixel accuracy measure15, which is analogous to the classification accuracy of traditional deep learning networks, the main difference being that the accuracy is computed on the pixel level instead of on the image level:
<img alt="Equation 1" src="/files/ftp_upload/61906/61906eq01.jpg" />
where PA is the pixel accuracy measure, nij&#160;is the number of pixels from class i predicted to belong to class j and ti =&#160;&#931;j nij is the total number of pixels from class i&#160;in the ground-truth labels1. In other words, the pixel accuracy measure is computed by dividing correctly predicted pixels by the total number of pixels. The final accuracy of the trained FCN-8s model was 92.18%. Figure 2 shows three example images from the fake-food image dataset (one from each of the training, validation and testing subsets), along with the corresponding ground-truth and model prediction labels.
The parameters to train the HTC20 ResNet-101 model for food image segmentation were set as follows: the solver type used was SGD because it outperformed other solver types. The base learning rate was set to 0.00125 and the batch size to 2 images. The number of training epochs was set to 40 per training phase, and multiple training phases were performed - first on the original FRC dataset without augmented images, then on the 8x-augmented and 4x-augmented FRC dataset multiple times in an alternating fashion, each time taking the best-performing model and fine-tuning it in the next training phase. More details on the training phases can be found in section 3.3&#160;of the protocol text. Finally, the step-down learning policy was used, with fixed epochs for when the learning rate decreased (epochs 28 and 35 for the first training phase). An important thing to note is that while this sequence of training phases produced the best results in our testing in the scope of the FRC, using another dataset might require a different sequence to produce optimal results.
This ResNet-based solution for food image segmentation was evaluated using the following precision measure19:
<img alt="Equation 2" src="/files/ftp_upload/61906/61906eq02.jpg" />
where P is precision, TP is the number of true positive predictions by the food image segmentation model, FP is the number of false positive predictions and IoU is Intersection over Union, which is computed with this equation:
<img alt="Equation 3" src="/files/ftp_upload/61906/61906eq03.jpg" />
where Area of Overlap&#160;represents the number of predictions by the model that overlap with the ground truth, and Area of Union represents the total number of predictions by the model together with the ground truth, both on a pixel level and for each individual food class. Recall is used as a secondary measure and is calculated in a similar way, using the following formula19:
<img alt="Equation 4" src="/files/ftp_upload/61906/61906eq04.jpg" />
where R is recall and FN is the number of false negative predictions by the food image segmentation model. The precision and recall measures are then averaged across all classes in the ground truth. Using these measures, our model achieved an average precision of 59.2% and an average recall of 82.1%, which ranked second in the second round of the Food Recognition Challenge19. This result was 4.2% behind the first place and 5.3% ahead of the third place in terms of the average precision measure. Table 1 contains the results for the top-4 participants in the competition.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61906/61906fig01.jpg" /> 
Figure 1: Diagram of the NutriNet deep neural network architecture. This figure has been published in Mezgec et al.2. <a href="https://www.jove.com/files/ftp_upload/61906/61906fig01large.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/61906/61906fig02.jpg" /> 
Figure 2: Images from the fake-food image dataset. Original images (left), manually-labelled ground-truth labels (middle) and predictions from the FCN-8s model (right). This figure has been published in Mezgec et al.1. <a href="https://www.jove.com/files/ftp_upload/61906/61906fig02large.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>Team Name</td>
			<td>Placement</td>
			<td>Average Precision</td>
			<td>Average Recall</td>
		</tr>
		<tr>
			<td>rssfete</td>
			<td>1</td>
			<td>63.4%</td>
			<td>88.6%</td>
		</tr>
		<tr>
			<td>simon_mezgec</td>
			<td>2</td>
			<td>59.2%</td>
			<td>82.1%</td>
		</tr>
		<tr>
			<td>arimboux</td>
			<td>3</td>
			<td>53.9%</td>
			<td>73.5%</td>
		</tr>
		<tr>
			<td>latentvec</td>
			<td>4</td>
			<td>48.7%</td>
			<td>71.1%</td>
		</tr>
	</tbody>
</table>
Table 1: Top-4 results from the second round of the Food Recognition Challenge. Average precision is taken as the primary performance measure and average recall as a secondary measure. Results are taken from the official competition leaderboard19.
Supplemental Files. <a href="https://www.jove.com/files/ftp_upload/61906/simon_mezgec_supplemental_files_revision1_.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Deep Neural Networks for Image-Based Dietary Assessment at JoVE.com

Deep Neural Networks for Image-Based Dietary Assessment

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 ...

deep-neural-networks-for-image-based-dietary-assessment

Research

JoVE Journal

Engineering

8.9K Views.  Jožef Stefan International Postgraduate School. 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.

Video: Deep Neural Networks for Image-Based Dietary Assessment

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 ...

Indoor Experimental Assessment of the Efficiency and Irradiance Spot of the Achromatic Doublet on Glass (ADG) Fresnel Lens for Concentrating Photovoltaics

We present a method to characterize achromatic Fresnel lenses for photovoltaic applications. The achromatic doublet on glass (ADG) Fresnel lens is composed of two materials, a plastic and an elastomer, whose dispersion characteristics (refractive index variation with wavelength) are different. We first designed the lens geometry and then used ray-tracing simulation, based on the Monte Carlo method, to analyze its performance from the point of view of both optical efficiency and the maximum attainable concentration. Afterwards, ADG Fresnel lens prototypes were manufactured using a simple and reliable method. It consists of a prior injection of plastic parts and a consecutive lamination, together with the elastomer and a glass substrate to fabricate the parquet of ADG Fresnel lenses. The accuracy of the manufactured lens profile is examined using an optical microscope while its optical performance is evaluated using a solar simulator for concentrator photovoltaic systems. The simulator is composed of a xenon flash lamp whose emitted light is reflected by a parabolic mirror. The collimated light has a spectral distribution and an angular aperture similar to the real Sun. We were able to assess the optical performance of the ADG Fresnel lenses by taking photographs of the irradiance spot cast by the lens using a charge-coupled device (CCD) camera and measuring the photocurrent generated by several types of multi junction (MJ) solar cells, which have been previously characterized at a solar simulator for concentrator solar cells. These measurements have demonstrated the achromatic behavior of ADG Fresnel lenses and, as a consequence, the suitability of the modelling and manufacturing methods.

Indoor Experimental Assessment of the Efficiency and Irradiance Spot of the Achromatic Doublet on Glass ADG Fresnel Lens for Concentrating Photovoltaics

The achromatic doublet on glass (ADG) Fresnel lens makes use of two materials with differing dispersion to reduce chromatic aberration and increase attainable concentration. In this paper, a protocol for the complete characterization of the ADG Fresnel lens is presented.

We present a method to characterize achromatic Fresnel lenses for photovoltaic applications. The achromatic doublet on glass (ADG) Fresnel lens is ...

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

The irregular and uneven residual sections along the length of a corroded steel bar substantially change its mechanical properties and significantly dominate the safety and performance of an existing concrete structure. As a result, it is important to measure the geometry and amount of corrosion of a steel bar in a structure properly to assess the residual bearing capacity and service life of the structure. This paper introduces and compares five different methods for measuring the geometry and amount of corrosion of a steel bar. A single 500 mm long and 14 mm diameter steel bar is the specimen that is subjected to accelerated corrosion in this protocol. Its morphology and the amount of corrosion were carefully measured before and after using mass loss measurements, a Vernier caliper, drainage measurements, 3D scanning, and X-ray micro-computed tomography (XCT). The applicability and suitability of these different methods were then evaluated. The results show that the Vernier caliper is the best choice for measuring the morphology of a non-corroded bar, while 3D scanning is the most suitable for quantifying the morphology of a corroded bar.

This paper measures the geometry and the amount of corrosion of a steel bar using different methods: mass loss, calipers, drainage measurements, 3D scanning, and X-ray micro-computed tomography (XCT).

The irregular and uneven residual sections along the length of a corroded steel bar substantially change its mechanical properties and significantly ...

Impacts of Free-falling Spheres on a Deep Liquid Pool with Altered Fluid and Impactor Surface Conditions

Vertical impacts of spheres on clean water have been the subject of numerous water entry investigations characterizing cavity formation, splash crown ascension and Worthington jet stability. Here, we establish experimental protocols for examining splash dynamics when smooth free-falling spheres of varying wettability, mass, and diameter impact the free surface of a deep liquid pool modified by thin penetrable fabrics and liquid surfactants. Water entry investigations provide accessible, easily assembled and executed experiments for studying complex fluid mechanics. We present herein a tunable protocol for characterizing splash height, flow separation metrics, and impactor kinematics, and representative results which might be acquired if reproducing our approach. The methods are applicable when characteristic splash dimensions remain below approximately 0.5 m. However, this protocol may be adapted for greater impactor release heights and impact velocities, which augurs well for translating results to naval and industry applications.

This protocol demonstrates the basic experimental configuration for water entry experiments with free-falling spheres. Methods for the alteration of liquid surface with penetrable fabrics, the preparation of chemically non-wetting spheres, and steps for splash visualization and data extraction are discussed.

Vertical impacts of spheres on clean water have been the subject of numerous water entry investigations characterizing cavity formation, splash crown ...

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

The Network Function Virtualization (NFV) paradigm is one of the key enabling technologies in the development of the 5th generation of mobile networks. This technology aims to lessen the dependence on hardware in the provision of network functions and services by using virtualization techniques that allow the softwarization of those functionalities over an abstraction layer. In this context, there is increasing interest in exploring the potential of unmanned aerial vehicles (UAVs) to offer a flexible platform capable of enabling cost-effective NFV operations over delimited geographic areas.
To demonstrate the practical feasibility of utilizing NFV technologies in UAV platforms, a protocol is presented to set up a functional NFV environment based on open source technologies, in which a set of small UAVs supply the computational resources that support the deployment of moderately complex network services. Then, the protocol details the different steps needed to support the automated deployment of an internet protocol (IP) telephony service over a network of interconnected UAVs, leveraging the capacities of the configured NFV environment. Experimentation results demonstrate the proper operation of the service after its deployment. Although the protocol focuses on a specific type of network service (i.e., IP telephony), the described steps may serve as a general guide to deploy other type of network services. On the other hand, the protocol description considers concrete equipment and software to set up the NFV environment (e.g., specific single board computers and open source software). The utilization of other hardware and software platforms may be feasible, although the specific configuration aspect of the NFV environment and the service deployment may present variations with respect to those described in the protocol.

The objective of the described protocol is twofold: to configure a network functions virtualization environment using unmanned aerial vehicles as computational entities providing the underlying structure to execute virtualized network functions and to use this environment to support the automated deployment of a functional internet protocol telephony service over the aerial vehicles.

The Network Function Virtualization (NFV) paradigm is one of the key enabling technologies in the development of the 5th generation of mobile networks. ...

Automated Delivery of Microfabricated Targets for Intense Laser Irradiation Experiments

Described is an experimental procedure that enables high-power laser irradiation of microfabricated targets. Targets are brought to the laser focus by a closed feedback loop that operates between the target manipulator and a ranging sensor. The target fabrication process is explained in detail. Representative results of MeV-level proton beams generated by irradiation of 600 nm thick gold foils at a rate of 0.2 Hz are given. The method is compared with other replenishable target systems and the prospects of increasing the shot rates to above 10 Hz are discussed.

A protocol is presented for automated irradiation of thin gold foils with high intensity laser pulses. The protocol includes a step-by-step description of the micromachining target fabrication process and a detailed guide for how targets are brought to the laser&#39;s focus at a rate of 0.2 Hz.

Described is an experimental procedure that enables high-power laser irradiation of microfabricated targets. Targets are brought to the laser focus by a ...

UV-Vis Spectroscopic Characterization of Nanomaterials in Aqueous Media

The physicochemical characterization of nanomaterials (NMs) is often an analytical challenge, due to their small size (at least one dimension in the nanoscale, i.e. 1&#8211;100 nm), dynamic nature, and diverse properties. At the same time, reliable and repeatable characterization is paramount to ensure safety and quality in the manufacturing of NM-bearing products. There are several methods available to monitor and achieve reliable measurement of nanoscale-related properties, one example of which is Ultraviolet-Visible Spectroscopy (UV-Vis). This is a well-established, simple, and inexpensive technique that provides non-invasive and fast real-time screening evaluation of NM size, concentration, and aggregation state. Such features make UV-Vis an ideal methodology to assess the proficiency testing schemes (PTS) of a validated standard operating procedure (SOP) intended to evaluate the performance and reproducibility of a characterization method. In this paper, the PTS of six partner laboratories from the H2020 project ACEnano were assessed through an interlaboratory comparison (ILC). Standard gold (Au) colloid suspensions of different sizes (ranging 5&#8211;100 nm) were characterized by UV-Vis at the different institutions to develop an implementable and robust protocol for NM size characterization.

This study presents the benchmarking results for an interlaboratory comparison (ILC) designed to test the standard operating procedure (SOP) developed for gold (Au) colloid dispersions characterized by ultraviolet-visible Spectroscopy (UV-Vis), amongst six partners from the H2020 ACEnano project for sample preparation, measurement, and analysis of the results.

The physicochemical characterization of nanomaterials (NMs) is often an analytical challenge, due to their small size (at least one dimension in the ...

3D Printing - Evaluating Particle Emissions of a 3D Printing Pen

Three-dimensional (3D) printing as a type of additive manufacturing shows continuing increase in application and consumer popularity. The fused filament fabrication (FFF) is an inexpensive method used most frequently by consumers. Studies with 3D printers have shown that during the printing process particulate and volatile substances are released. Handheld 3D printing pens also use the FFF method but the consumer&#8217;s proximity to the 3D pens gives reason to higher exposure compared to a 3D printer. At the same time, 3D printing pens are often marketed for children who could be more sensitive to the printing emission. The aim of this study was to implement a low cost method to analyze the emissions of 3D printing pens. Polylactide (PLA) and acrylonitrile butadiene styrene (ABS) filaments of different colors were tested. In addition, filaments containing metal and carbon nanotubes (CNTs) were analyzed. An 18.5 L chamber and sampling close to the emission source was used to characterize emissions and concentrations near the breathing zone of the user.
Particle emissions and particle size distributions were measured and the potential release of metal particles and CNTs investigated. Particle number concentrations were found in a range of 105 - 106 particles/cm3, which is comparable to previous reports from 3D printers. Transmission electron microscopy (TEM) analysis showed nanoparticles of the different thermoplastic materials as well as of metal particles and CNTs. High contents of metal were observed by inductively coupled plasma mass spectrometry (ICP-MS).
These results call for a cautious use of 3D pens due to potential risk to the consumers.

This protocol presents a method to analyze the emission of 3D printing pens. Particle concentration and particle size distribution of the released particle is measured. Released particles are further analyzed with transmission electron microscopy (TEM). Metal content in filaments is quantified by inductively coupled plasma mass spectrometry (ICP-MS).

Three-dimensional (3D) printing as a type of additive manufacturing shows continuing increase in application and consumer popularity. The fused filament ...

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Network Function Virtualization (NFV) has been regarded as one of the key enablers for the 5th Generation of mobile networks, or 5G. This paradigm allows to reduce the dependence on specialized hardware to deploy telecommunications and vertical services. To this purpose, it relies on virtualization techniques to softwarize network functions, simplifying their development and reducing deployment time and costs. In this context, Universidad Carlos III de Madrid, Telef&#243;nica, and IMDEA Networks Institute have developed an NFV ecosystem inside 5TONIC, an open network innovation center focused on 5G technologies, enabling the creation of complex, close to reality experimentation scenarios across a distributed set of NFV infrastructures, which can be made available by stakeholders at different geographic locations. This article presents the protocol that has been defined to incorporate new remote NFV sites into the multi-site NFV ecosystem based on 5TONIC, describing the requirements for both the existing and the newly incorporated infrastructures, their connectivity through an overlay network architecture, and the steps necessary for the inclusion of new sites. The protocol is exemplified through the incorporation of an external site to the 5TONIC NFV ecosystem. Afterwards, the protocol details the verification steps required to validate a successful site integration. These include the deployment of a multi-site vertical service using a remote NFV infrastructure with Small Unmanned Aerial Vehicles (SUAVs). This serves to showcase the potential of the protocol to enable distributed experimentation scenarios.

The objective of the described protocol is to support the flexible incorporation of 5G experimentation infrastructures into a multi-site NFV ecosystem, through a VPN-based overlay network architecture. Moreover, the protocol defines how to validate the effectiveness of the integration, including a multi-site vertical service deployment with NFV-capable small aerial vehicles.

Network Function Virtualization (NFV) has been regarded as one of the key enablers for the 5th Generation of mobile networks, or 5G. This paradigm allows ...

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Aortic regurgitation (AR) refers to backward blood flow from the aorta into the left ventricle (LV) during ventricular diastole. The regurgitant jet arising from the complex shape is characterized by the three-dimensional flow and high-velocity gradient, sometimes limiting an accurate measurement of the regurgitant volume using 2D echocardiography. Recently developed four-dimensional flow magnetic resonance imaging (4D flow MRI) enables three-dimensional volumetric flow measurements, which can be used to accurately quantify the amount of the regurgitation. This study focuses on (i) magnetic resonance compatible AR model fabrication (dilatation, perforation, and prolapse) and (ii) systematic analysis of the performance of 4D flow MRI in AR quantification. The results indicated that the formation of the forward and backward jets over time was highly dependent on the types of AR origin. The amount of regurgitation volume bias for the model types were -7.04%, -33.21%, 6.75%, and 37.04% compared to the ground truth (48 mL) volume measured from the pump stroke volume. The largest error of the regurgitation fraction was around 12%. These results indicate that careful selection of imaging parameters is required when absolute regurgitation volume is important. The suggested in vitro flow phantom can easily be modified to simulate other valvular diseases such as aortic stenosis or bicuspid aortic valve (BAV) and can be used as a standard platform to test different MRI sequences in the future.

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Aortic regurgitation is an aortic valve heart disease. This manuscript demonstrates how four-dimensional flow magnetic resonance imaging can evaluate aortic regurgitation using in vitro heart valves mimicking aortic regurgitation.

Aortic regurgitation (AR) refers to backward blood flow from the aorta into the left ventricle (LV) during ventricular diastole. The regurgitant jet ...