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This work is focused on the image processing using the OpenCV library and detectingmoving objects in video using convolutional neural networks. The created application can detectmoving objects in the video and contains additional functionality. The application includes theYOLO convolutional model, which helps to detect moving objects.

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.

Phoebe Apperson Hearst měla velmi úspěšného vlastního syna Williama, který byl ale více jako jeho otec: tvrdý obchodník. Našla však jemnou, uměleckou duši v malíři Orrinu Peckovi (1860–1921), který byl údajně gay a který ji, ještě za života své vlastní matky, začal oslovovat „má druhá mámo.“ Na základě podrobného výzkumu jejich vzájemné korespondence v Peckově pozůstalosti se můžeme ptát, jak moc si byla progresivní, bohatá žena 19. století, jakou byla Phoebe Hearst, vědoma Peckovy sexuality a pokud ano, jestli s tím neměla problém, nebo šlo o nevyřčené tajemství mezi nimi? Jejich příběh představí historik umění Ladislav Zikmund-Lender. Phoebe Apperson Hearst had a very successful son of William, but he was more like his father: a tough businessman. However, she found a delicate, artistic soul in the painter Orrin Peck (1860–1921), who was allegedly gay and who, while still his own mother's life, began to address her as “my second mother.” Based on a detailed study of their correspondence in Peck's estate, we may ask how much a progressive, rich 19th-century woman like Phoebe Hearst was aware of Peck's sexuality, and if so, if she had no problem with it, or was it an unspoken secret between them? Their story will be presented by art historian Ladislav Zikmund-Lender.

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).

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.

This paper presents an evaluation of deep learning architectures designed for modulationrecognition. The evaluation inspects, whether the architectures behave in the same way as they didon the dataset they were designed on. The architectures are trained and tested on two different radiomodulation datasets. This results in proposing additional binary classification as a method to reducemisclassification of QAM modulation types in one of the datasets.

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.

Automatic segmentation of the biological structures in micro-CT data is still a challengesince the object of interest (craniofacial cartilage in our case) is commonly not characterized by uniquevoxel intensity or sharp borders. In recent years, convolutional neural networks (CNNs) have becomeexceedingly popular in many areas of computer vision. Specifically, for biomedical image segmentationproblems, U-Net architecture is widely used. However, in case of micro-CT data, there isa question whether 3D CNN would not be more beneficial. This paper introduces CNN architecturebased on V-Net as well as the methodology for data preprocessing and postprocessing. The baselinearchitecture was further optimized using advanced techniques such as Atrous Spatial Pyramid Pooling(ASPP) module, Scaled Exponential Linear Unit (SELU) activation function, multi-output supervisionand Dense blocks. For network learning, modern approaches were used including learning ratewarmup or AdamW optimizer. Even though the 3D CNN do not outperform U-Net regarding the craniofacialcartilage segmentation, the optimization raises the median of Dice coefficient from 69.74 %to 80.01 %. Therefore, utilizing these advanced techniques is highly encouraged as they can be easilyadded to any U-Net-like architecture and may remarkably improve the results.

The article deals with basic concepts in the sector of image processing using neural networks.It describes the basic principles and methods of object recognition and image segmentationusing neural network architectures based on R-CNN architecture. Specifically the work focuses onhuman body segmentation from static images, resulting in individual segments in the output maskcorresponding to each limb of a human body

The paper deals with description, design, learning and inference process of a convolutional 2D neural network for detection of shortened winding turns of a three-phase permanent magnet synchronous motor. Input datasets for aforementioned procedures have been created by sensing vibration data on the real motor using accelerometers with a possibility of artificially induce short circuit in the motor winding. Only simple pre-processing of a time signal has been done – the time waveform was reshaped into 2D greyscale images with a size of 64 x 64 points and led directly into the neural network. No pretrained network has been used – internal parameters have been learned from scratch. Learning process as well as inference of the network have been performed on a standard personal computer with nVidia GeForce RTX 2080 Ti graphics card and implemented usign Python in Keras/TensorFlow environment. Datasets for different working states of the motor, such as speed, torque, error type and its severity have been used. Training procedure of the network has been done within lower tens of minutes and final validation accuracy was 100 % in the most cases, while classification accuracy during inference process has reached the value of more than 99 %. Obtained results confirmed a fact, that faults’ detection of the mechatronic system based on sensing of mechanical quantities and their evaluation is very reliable even in the case of electrical-based faults.