Towards secure judgments aggregation in AHP

In the decision making methods the common assumption is the honesty and professionalism of experts. However, this is not the case when one or more experts in the group decision making framework, such as the group analytic hierarchy process (GAHP), try to manipulate results in their favor. The aim of this paper is to introduce two heuristics in the GAHP setting allowing to detect the manipulators and minimize their effect on the group consensus by diminishing their weights. The first heuristic is based on the assumption that manipulators will provide judgments which can be considered outliers with respect to judgments of the rest of the experts in the group. Second heuristic assumes that dishonest judgments are less consistent than average consistency of the group. Both approaches are illustrated with numerical examples and simulations.Comment: 32 page

Synergy of Physics-based Reasoning and Machine Learning in Biomedical Applications: Towards Unlimited Deep Learning with Limited Data

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by training-data incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data

LiDAR: reconfigurable hardware based data acquisition

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores (área de especialização em Sistemas Embebidos e Computadores)There is an expected increase in the demand for Advanced Driver-Assistance Systems (ADAS) over the next decade, incited by regulatory and consumer interest in safety applications that protect drivers and reduce accidents [1]. Even though ADAS applications are still beginning, both the OEMs and their suppliers are realizing that they could become one of the essential characteristics differentiating the various automotive brands, consequently, one of their most important revenue sources. Furthermore, the technologies used in ADAS could be used in the future to create fully autonomous vehicles, which are now becoming a major focus of research and development. There are three main sensor solutions used in ADAS. Firstly, there are optical sensors and camera based-solutions. These are the most versatile and cost-efficient solutions. However, they are easily affected by poor weather and other environmental hazards. Furthermore, they require complex software algorithms to recognize objects [1]. The second solution incorporates short and long range Radars for determining the distance, speed, and direction of objects. These sensors work better than the others in adverse weather conditions. Nonetheless there is typically a compromise between the measurement range and angle [1]. The last type of solution involves using LiDAR systems, which use laser pulses to scan the surroundings and generate a complete and precise three-dimensional image of the environment. The LiDAR is less sensitive to light and weather conditions than optical systems and provides the location of the surrounding objects directly. Due to the ever-growing use of ADAS, there is a need to develop a more advanced LiDAR sensor. To answer that need and to overcome some of the limitations of the current LiDAR sensors, the Chassis Systems Control of the Bosch Group is developing an automotive LiDAR, and the current Master’s thesis is integrated in the project. In this Master’s thesis, an Acquisition System for Bosch’s LiDAR sensor was developed. For measuring the Time-of-Flight of the laser pulses of the LiDAR, to do so multiple TDC Peripherals were developed in an FPGA platform. The measurement precision of the developed Acquisition System varies between 232.17 ps and 188.66 ps, with an average precision of 207.47 ps.É expectável que nas próximas décadas exista um aumento na procura das ADAS, potenciado pelos interesses dos reguladores e dos consumidores em aplicações que protejam o condutor e reduzam o número de acidentes. Tanto os OEMs, como os seus fornecedores aperceberam-se que, apesar das ADAS ainda estarem numa fase inicial, podem-se tornar uma característica diferenciadora entre as diversas marcas de automóveis, e por isso, uma das suas principais fontes de rendimento. Além disso, as tecnologias usadas nas ADAS poderão vir a ser utilizadas para criar veículos autónomos, os quais se estão a revelar como um dos principais focos da pesquisa e desenvolvimento. Existem três principais soluções de sensores usadas nas ADAS. Primeiro, existem as soluções baseadas em sensores óticos, que são as soluções mais versáteis e económicas. No entanto, este tipo de soluções é facilmente afetado pelo mau tempo e outros fatores ambientais. Para além do facto de necessitarem o uso de algoritmos complexos para reconhecerem objectos. A segunda solução incorpora o uso de RADARs de longo e curto alcance, com o objetivo de determinar a distância, velocidade e direção dos objetos. Estes sensores são pouco afetados por condições meteorológicas adversas. Porém, existe um compromisso entre o alcance e o ângulo de medição do sensor. A última solução envolve o uso de sistemas de LiDAR. Estes sistemas usam pulsos de laser para examinar meio-envolvente, de modo a gerar uma imagem tridimensional completa do mesmo. O LiDAR é menos sensível à luz e às condições meteorológicas e consegue fornecer diretamente a localização dos objetos à sua volta. Devido à crescente utilização das ADAS, existe a necessidade de desenvolver sensores LiDAR mais avançados. Para suprir essa necessidade e para ultrapassar algumas das limitações dos sensores atuais, a divisão Chassis Systems Control, do grupo Bosch, está atualmente a desenvolver uma solução de um sensor LiDAR para a indústria automóvel, projeto onde se insere esta dissertação. Nesta dissertação foi desenvolvido um Sistema de Aquisição para o sensor LiDAR. Este sistema mede o TOF dos pulsos de laser usado pelo LiDAR. Para isso, vários periféricos de TDC foram desenvolvidos numa FPGA. A precisão de medição do sistema varia entre os 232.17 ps e os 188.66 ps, com um valor médio de 207.47 ps.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Znajdywanie odpowiedniości punktów charakterystycznych na obrazach stereowizyjnych

Doctoral Theses: 1. Dedicated Methods of Analysis and Processing of Stereovision Images Enable to Improve a Quality of Stereo Correspondence Results. 2. Introduction of New Attributes of Feature Points Allows to Improve the Efficiency of the Stereo Correspondence Algorithms. The aim of the dissertation is to develop a method that allows to find matches between feature points on stereo vision images. Proposed method is dedicated to medical applications. Achieving the goal of the dissertation required several partial goals. 1. Developing of medical images segmentation method. 2. Developing of matching method. 3. Evaluation of matching quality. 4. Evaluation of influence of the selected feature points attributes on stereo correspondence quality. 5. Final optimization of algorithms to work on subcutaneous vessels images. Results presented in the thesis. • Three image segmentation methods BGOM, SSSB and BLG. • Three matching methods MED, MED-NDD and MED-RGB. • First method (MED) is based on the assumption that the correct disparity is the minimal distance between given feature point on the left image and all feature points on the right image in the given row. • Second method (MED-NDD) improves the results obtained with the first method by 30% for selected images. • Third method (MED-RGB) improves the results obtained with the first method by 30% for selected images. • Quality evaluation of the created disparity map accordingly to three accuracy criteria and reference ground true disparity map. • Evaluation of influence of the new feature point attributes to stereo correspondence results. Attributes were properly selected improving an average results by 12%. However, to clearly state the values of error-minimizing attributes would have to be carried out further research on properly prepared image database. • Dedicated algorithms to cope with medical images of subcutaneous vessels that present much better effectiveness in comparison to existing widely used methods. • Proposed in dissertation algorithms are the quickest what follows from the presented comparisons. The methods were tested on three groups of test images namely solids, subcutaneous vessels and real images from the Middlebury College Stereo Vision library.. Based on the above, it must be stated that the conducted research confirms the correctness of the theses formulated at the beginning of the work

Design of an adaptive congestion control protocol for reliable vehicle safety communication

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