Drip bags are used in hospital environments to administerdrugs and nutrition to patients. Ensuring that they are usedcorrectly and are refilled in time are important for the safetyof patients. This study examines the use of a ConvolutionalNeural Network (CNN) to monitor the fluid levels of drip bagsvia image recognition to potentially form the base of an earlywarning system, and assisting in making medical care moreefficient. Videos of drip bags were recorded as they wereemptying their contents in a controlled environment and fromdifferent angles. A CNN was built to analyze the recordeddata in order to predict a bags fluid level with a 5% intervalprecision from a given image. The results show that the CNNused performs poorly when monitoring fluid levels in dripbags.

Automated Guided Vehicle(AGV) as a kind of material conveying equipment has been widely used in modern manufacturing systems. [1] It carries the goods between the workshop along the designated paths. The ability of localization and recognizing the environment around themselves is the essential technology. AGV navigation is developed from several technologies such as fuzzy theory, neural network and other intelligent technology. Among them, visual navigation is one of the newer navigations, because of its path laying is easy to maintain, can identify variety of road signs. Compared with traditional methods, this approach has a better flexibility and robustness, since it can recognition more than one path branch with high anti-jamming capability. Recognizing the environment from imagery can enhance safety and dependability of an AGV, make it move intelligently and brings broader prospect for it. University West has a Patrolbot which is an AGV robot with basic functions. The task is to enhance the ability of vision analysis, to make it become more practical and flexible. The project is going to add object detection, object recognition and object localization functions on the Patrolbot. This thesis project develops methods based on image recognition, deep learning, machine vision, Convolution Neural Network and related technologies. In this project Patrolbot is a platform to show the result, we can also use this kind of program on any other machines. This report generally describes methods of navigation, image segmentation and object recognition. After analyzing the different methods of image recognition, it is easy to find that Neural Network has more advantages for image recognition, it can reduce the parameters and shorting the training and analyzing time, therefore Convolution Neural Network was introduced detailly. After that, the way to achieve image recognition using convolution neural network was presented and in order to recognize several objects at the same time, an image segmentation was also presented here. On the other hand, to make this image recognition processes to be used widely, the ability of transfer learning becomes important. Therefore, the method of transfer learning is presented to achieve customized requirement.

The company Ericsson is taking steps towards embracing automating techniques and applying them to their product development cycle. Ericsson wants to apply machine learning techniques to automate the evaluation of a text categorization problem of error reports, or trouble reports (TRs). An excess of 100,000 TRs are handled annually. This thesis presents two possible solutions for solving the routing problems where one technique uses traditional classifiers (Multinomial Naive Bayes and Support Vector Machines) for deciding the route through the company hierarchy where a specific TR belongs. The other solution utilizes a Convolutional Neural Network for translating the TRs into low-dimensional word vectors, or word embeddings, in order to be able to classify what group within the company should be responsible for the handling of the TR. The traditional classifiers achieve up to 83% accuracy and the Convolutional Neural Network achieve up to 71% accuracy in the task of predicting the correct class for a specific TR.

In this paper two convolutional neural networks are estimated to classify whether an image contains a swastika or not. The images are gathered from the gaming platform Steam and by scraping a web search engine. The architecture of the networks is kept moderate and the difference between the models is the final layer. The first model uses an average type operation while the second uses the conventional fully-connected layer at the end. The results show that the performance of the two models is similar and the test error is in the 6-9 % range.

<p>In this project, CNN has been applied as a training tool to process image classification and object avoidance on remote robotic cars built with the Nvidia Jetson Nano developer kit. The kit was programmed using the wireless programming environment, Jupyter notebook. In addition, two different CNN models have been applied to analyze the output result performance. The main purpose is to train the robot to identify objects and improve its accuracy. The recognition and accuracy rate under different conditions can be observed by comparing the two models with different graphic inputs conditions. This project adopts the pre-train model for real time demonstrations and can be executed in a cloudless environment (without networks involved). As a result, the robot can achieve a high accuracy rate in both CNN models output result performance. Moreover, the pre train model can execute in local service to accomplish cloudless.</p>

Video super-resolution (VSR) methods align and fuse consecutive low-resolution frames to generate high-resolution frames. One of the main difficulties for the VSR process is that video contains various motions, and the accuracy of motion estimation dramatically affects the quality of video restoration. However, standard CNNs share the same receptive field in each layer, and it is challenging to estimate diverse motions effectively. Neuroscience research has shown that the receptive fields of biological visual areas will be adjusted according to the input information. Diverse receptive fields in temporal and spatial dimensions have the potential to adapt to various motions, which is rarely paid attention in most known VSR methods. In this thesis, we propose to provide adaptive receptive fields for the VSR model. Firstly, we design a multi-kernel 3D convolution network and integrate it with a multi-kernel deformable convolution network for motion estimation and multiple frames alignment. Secondly, we propose a 2D multi-kernel convolution framework to improve texture restoration quality. Our experimental results show that the proposed framework outperforms the state-of-the-art VSR methods.

In recent years, the tech is moving towards a more automized and smarter software. To achieve smarter software the implementation of AI is a step towards that goal. The forest industry needs to become more automized and decrease the manual labor. Decreasing manual labor will both have a positive impact on both the cost and the environment. After doing a literature study the conclusion was to use Mask R-CNN to be able to make the AI learn about the pattern of the different stands. The different stands were extracted and masked for the Mask R-CNN. First there was a comparison between the usage of a computer versus Google Colab, and the results show that Google Colab did deliver the results a little faster than on the computer. Using a smaller area with fewer stands gave a better result and decreased the risk of the algorithm crashing. Using 42 areas with about 10 stands in each gave better results than using one big area with 3248 stands. Using 42 areas gave the result of an average IoU of 42%. Comparing this to 6 areas with about 10 stands each gave the result of 28% IoU. The result of increasing the data split to 70/30 did gave the best IoU with the value of 47%.

This thesis is concerned with future trend prediction on capital markets on the basis of neural networks. Usage of convolutional and recurrent neural networks, Elliott wave theory and scalograms for capital market's future trend prediction is discussed. The aim of this thesis is to propose a novel approach to future trend prediction based on Elliott's wave theory. The proposed approach will be based on the principle of classification of chosen patterns from Elliott's theory by the way of convolutional neural network. To this end scalograms of the chosen Elliott patterns will be created through application of continuous wavelet transform on parts of historical time series of price for chosen stocks.

This diploma thesis deals with the detection of fire and smoke from the image signal. The approach of this work uses a combination of convolutional and recurrent neural network. Machine learning models created in this work contain inception modules and blocks of long short-term memory. The research part describes selected models of machine learning used in solving the problem of fire detection in static and dynamic image data. As part of the solution, a data set containing videos and still images used to train the designed neural networks was created. The results of this approach are evaluated in conclusion.

The work deals with the generation of ECG signals using generative adversarial networks (GAN). It examines in detail the basics of artificial neural networks and the principles of their operation. It theoretically describes the use and operation and the most common types of failures of generative adversarial networks. In this work, a general procedure of signal preprocessing suitable for GAN training was derived, which was used to compile a database. In this work, a total of 3 different GAN models were designed and implemented. The results of the models were visually displayed and analyzed in detail. Finally, the work comments on the achieved results and suggests further research direction of methods dealing with the generation of ECG signals.