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In this paper, we present two pipelines in order to reduce the feature space for anomalydetection using the One Class SVM. As a first stage of both pipelines, we compare the performanceof three convolutional autoencoders. We use the PCA method together with t-SNE as the first pipelineand the reconstruction errors based method as the second. Both methods have potential for theanomaly detection, but the reconstruction error metrics prove to be more robust for this task. Weshow that the convolutional autoencoder architecture doesn’t have a significant effect for this task andwe prove the potential of our approach on the real world dataset.

We propose the model combining convolutional neural network with multiple instancelearning in order to localize the premature atrial contraction and premature ventricular contraction.The model is based on ResNet architecture modified for 1D signal processing. Model was trainedon China Physiological Signal Challenge 2018 database extended by manually labeled ground truthpositions of premature complexes. The presented method did not reach satisfying results in PAClocalization (with dice = 0.127 for avg-pooling implementation). On the other hand, results of localizationof PVCs were comparable with other published studies (with dice = 0.952 for avg-poolingimplementation).

In 1939, due to WWII and the Nuremberg Laws, the revolutionary Czech structural engineer Jaroslav J. Polívka arrived in the United States. After his arrival, he started a research job at UC Berkeley, renewed his engineering practice, and offered his services to the US military as many businesspersons did during this era. Polívka worked for Henry Kaiser who turned Richmond, CA, into a vibrant, fast developing workers city. New residential districts, hospitals, hangars, docks, and warehouses were built there. Henry Kaiser approached the structural development of the city in the same way he revolutionized the construction of battleships: from prefabricated, standardized parts. He supported research and development of new technologies. Mobile, round-shaped hospitals from prefabricated aluminum frames were one of the results of that research. In 1946, Jaroslav J. Polívka introduced himself to the “starchitect” Frank Lloyd Wright. A productive mutual co-operation that lasted 13 years and resulted in eight spectacular projects had started and Polívka, who had been working on extensive research both at UC Berkeley and Stanford University, came up with many technological, structural, and material innovations over the period. In 1957, Henry Kaiser funded the construction of one of the two geodesic domes designed by Richard Buckminster Fuller in Hawaii and in the process he invited Frank Lloyd Wright to consult the project. Jaroslav J. Polívka was probably in direct contact with Fuller, since he wanted to include him in his unfinished project of an encyclopedia of the world-famous structural engineers. On this particular story and a social matrix evolving around Henry Kaiser and Frank Lloyd Wright, the lecture seeks to rethink architectural global modernism as a cooperative project rather than a series of individual innovations manifested by isolated genius figures.

The thesis deals with the image processing and detection of specific objects in the image. The main objective of this work is to implement a algoritm for the control of pipetting processes based on images from the camera. In this thesis is used for image processing an open source computer vision library OpenCV and for pipette detection is used convolutional neural network. The proposed solution is still in development process. The following article describes the issue and the results of the thesis solution.

To protect the vineyards from starlings is very costly and ineffective with the available resources. In my work, I have designed a detection module which consist of four microphones, each one is directed to the cardinal point and based on the intensity of sound, the module will provide the information about the direction where the flock is located, in the vineyard that passes to the control module and scares the flocks. The detection module processes the signal in a Raspberry Pi 4 single board computer using an artificial neural network algorithm.