Distributed genetic algorithm implementation by means of Remote Methods Invocation technique – Java RMI

The aim of this work is distributed genetic algorithm implementation (so called islandalgorithm) to accelerate the optimum searching process in space of solutions. The distributedgenetic algorithm has also smaller chances to fall in local optimum. This conception depends onmutual cooperation of the clients who perform separate work of genetic algorithms on localmachines. As a tool for implementation of distributed genetic algorithm, created to produce netapplication Java technology was chosen. In Java technology, there is a technique of remotemethods invocation – Java RMI. By means of invoking remote methods, objects between theclients and the server RMI can be sent.To test the work of genetic algorithm, searching for maximum function of two variables whichpossess a lot of local maxima and can be written by means of mathematical formula was chosen.The work of the whole system depends on existence of the server on which there are registeredremote services (methods) RMI and clients, each one on a separate machine. Each of the clientshas two threads, one of them accomplishes the work of local genetic algorithm whilst the otheraccomplishes the communication with the server. It sends to the server a new best individualwhich was found by the local genetic algorithm and takes the server form with the individuals, leftthere by other clients.To sum up there was created an engine of distributed genetic algorithm which searches themaximum of function and after a not large modification can be used to solve every optimizationproblem

NOWY ALGORYTM HYBRYDOWY WYKORZYSTUJĄCY AUTOENKODER KONWOLUCYJNY Z SVM DLA ELEKTRYCZNEJ TOMOGRTAFII IMPEDANCYJNEJ I TOMOGRAFII ULTRADŹWIĘKOWEJ

This paper presents a new hybrid algorithm using multiple Support Vector Machines models with convolutional autoencoder to Electrical Impedance Tomography, and Ultrasound Computed Tomography image reconstruction. The ultimate hybrid solution uses multiple SVM models to convert input measurements to individual autoencoder codes representing a given scene then the decoder part of the autoencoder can reconstruct the sceneArtykuł przedstawia nowy hybrydowy algorytm który używa modeli maszyn wektorów nośnych wraz z autoenkoderem konwolucyjnym do rekonstrukcji obrazu z Elektrycznej Tomografii Impedancyjnej oraz Ultrasonograficznej Tomografii Transmisyjnej. Ostateczne rozwiązanie hybrydowe używa wielu modeli SVM do konwersji pomiarów wejściowych do pojedynczych kodów autoenkodera reprezentujących daną scenę a wtedy dekoder wycięty z autoenkodera może zrekonstruować daną scen

Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes

This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to encode face images from a camera and the Multinomial Naïve Bayes for autonomous training in the real-time classification of persons. Faces of several persons visible in a camera are tracked using special cognitive tracking agents who deal with machine learning models. After a face in a new position of the frame appears (in a place where there was no face in the previous frames), the system checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is unknown, the system automatically starts training. As a result of the conducted experiments, one can conclude that good conditions provide assurance that the system can learn the faces of a new person who appears in the frame correctly. Based on our research, we can conclude that the critical element of this system working is the novelty detection algorithm. If false novelty detection works, the system can assign two or more different identities or classify a new person into one of the existing groups

Mozaikowanie obrazów z kapsuły endoskopowej

This paper presents numerical research and experiments giving rise to developed algorithm to connect into form of mosaic, images from the capsule endoscopy. In order to apply the algorithm, combined images must have a common area where the correspondence of points is determined. That allows to determine the transformation parameters to compensate movement of the capsule that occurs between moments when the mosaic images were acquired. The developed algorithm for images from the capsule endoscopy has proved to be faster and comparably accurate as commercial GDB-ICP algorithm.W artykule przedstawiono badania i eksperymenty numeryczne, będące podstawą opracowanego algorytmu łączenia, do formy mozaiki, obrazów z kapsuły endoskopowej. Warunkiem stosowania algorytmu jest, aby łączone obrazy posiadały wspólny obszar, w którym wyznaczana jest odpowiedniość punktów, pozwalająca z kolei na wyznaczenie parametrów transformacji kompensującej ruch kapsuły występujący pomiędzy chwilami, w których pozyskane były mozaikowane obrazy. Opracowany algorytm, w przypadku obrazów z kapsuły endoskopowej, okazał się szybszy i porównywalnie dokładny jak komercyjny algorytm GDB-ICP