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EN
The recent rapid improvement of nautical equipment functionality allows one to better observe and predict the dangers related to seamanship. However, these new features come with added complexity, and large amounts of information can overwhelm vessel crews and fleet operation centers, and the current state-of-the-art tools cannot filter out only the most important data for a given time and location. This paper presents the concepts and the algorithms of a software suite that provides a user with problem-oriented advice about a particular risk endangering a vessel and its crew. Based on the calculated navigational dangers and their predicted development, actionable guidance is proposed in an easy-to-understand human language. The quality of good seamanship is improved by a holistic approach to vessel installation, automated fleet operation center priority queuing, and the evaluation of crew performance during simulator training and daily operations. Both the software user interface, as well as the insights provided by the algorithm, are discussed.
EN
Digital signal processing, such as filtering, information extraction, and fusion of various results, is currently an integral part of advanced medical therapies. It is especially important in neurosurgery during deep-brain stimulation procedures. In such procedures, the surgical target is accessed using special electrodes while not being directly visible. This requires very precise identification of brain structures in 3D space throughout the surgery. In the case of deep-brain stimulation surgery for Parkinson’s disease (PD), the target area—the subthalamic nucleus (STN)—is located deep within the brain. It is also very small (just a few millimetres across), which makes this procedure even more difficult. For this reason, various signals are acquired, filtered, and finally fused, to provide the neurosurgeon with the exact location of the target. These signals come from preoperative medical imaging (such as MRI and CT), and from recordings of brain activity carried out during surgery using special brain-implanted electrodes. Using the method described in this paper, it is possible to construct a decision-support system that, during surgery, analyses signals recorded within the patient’s brain and classifies them as recorded within the STN or not. The constructed classifier discriminates signals with a sensitivity of 0.97 and a specificity of 0.96. The described algorithm is currently used for deep-brain stimulation surgeries among PD patients.
3
Content available Radar data fusion in the STRADAR system
EN
The main task of the Polish Border Guard is protection of the country’s border which requires utilization of multimedia surveillance systems automatically gathering, processing and sharing various data. The paper presents such a system developed for the Maritime Division of the Polish Border Guard within the STRADAR project and the problem of fusion of radar data in this system. The system, apart from providing communication means, gathers data from AIS, GPS and radar receivers: ARPA and SCANTER 2001. In the paper the concept of the radar data gathering in STRADAR system is provided with detailed presentation of radar servers, Radar INT modules and a reduplication (fusion) module and the proposition of the algorithm for radar data fusio.
PL
Głównym zadaniem polskiej Straży Granicznej jest ochrona granicy kraju, która wymaga wykorzystania multimedialnych systemów nadzoru umożliwiającychautomatyczne gromadzenie,przetwarzanie i udostępnianie różnego rodzaju danych. W artykule przedstawiono taki system opracowany dla Morskiego Oddziału Straży Granicznej w ramach projektu STRADAR oraz problem fuzji danych radarowych w tym systemie. System STRADAR, oprócz zapewnienia komunikacji pomiędzy elementami systemu, gromadzi i udostępniania dane z AIS, GPS i odbiorników radarowych: ARPA i SCANTER 2001. W artykule zaprezentowano koncepcjęgromadzenia danych adarowych w systemie STRADAR, przedstawiono serwery radarowe, moduł radar INT i moduł reduplikacjioraz zaproponowano algorytm fuzji danych radarowych.
PL
W artykule przedstawiono wyniki oryginalnych badań nad zastosowaniem sieci neuronowej wykorzystującej techniki głębokiego uczenia w zadaniu identyfikacji tożsamości na podstawie obrazów twarzy zarejestrowanych w zakresie widzialnym i w podczerwieni. W badaniach użyte zostały obrazy twarzy eksponowanych w zmiennych ale kontrolowanych warunkach. Na podstawie uzyskanych wyników można stwierdzić, że oba badane zakresy spektralne dostarczają istotnych ale różnych informacji o tożsamości danej osoby, które się wzajemnie uzupełniają.
EN
The paper presents the results of the original research on the application of a neural network using deep learning techniques in the task of identity recognition on the basis of facial images acquired in both visual and thermal radiation ranges. In the research, the database containing images acquired in various but controlled conditions was used. On the basis of the obtained results it can be established that both investigated spectral ranges provide distinctive and complementary details about the identity of an examined person.
EN
Earth’s atmosphere is monitored by a multitude of sensors. It is the troposphere that is of crucial importance for human activity, as it is there that the weather phenomena take place. Weather observations are performed by surface sensors monitoring, inter alia, humidity, temperature and winds. In order to observe the developments taking place in the atmosphere, especially in the clouds, weather radars are commonly used. They monitor severe weather that is associated with storm clouds, cumulonimbuses, which create precipitation visible on radar screens. Therefore, radar images can be utilized to track storm clouds in a data fusion system. In this paper an algorithm is developed for the extraction of blobs (interesting areas in radar imagery) used within data fusion systems to track storm cells. The algorithm has been tested with the use of real data sourced from a weather radar network. 100% of convection cells were detected, with 90% of them being actual thunderstorms.
6
Content available IoT sensing networks for gait velocity measurement
EN
Gait velocity has been considered the sixth vital sign. It can be used not only to estimate the survival rate of the elderly, but also to predict the tendency of falling. Unfortunately, gait velocity is usually measured on a specially designed walk path, which has to be done at clinics or health institutes. Wearable tracking services using an accelerometer or an inertial measurement unit can measure the velocity for a certain time interval, but not all the time, due to the lack of a sustainable energy source. To tackle the shortcomings of wearable sensors, this work develops a framework to measure gait velocity using distributed tracking services deployed indoors. Two major challenges are tackled in this paper. The first is to minimize the sensing errors caused by thermal noise and overlapping sensing regions. The second is to minimize the data volume to be stored or transmitted. Given numerous errors caused by remote sensing, the framework takes into account the temporal and spatial relationship among tracking services to calibrate the services systematically. Consequently, gait velocity can be measured without wearable sensors and with higher accuracy. The developed method is built on top of WuKong, which is an intelligent IoT middleware, to enable location and temporal-aware data collection. In this work, we present an iterative method to reduce the data volume collected by thermal sensors. The evaluation results show that the file size is up to 25% of that of the JPEG format when the RMSE is limited to 0.5º.
EN
A prominent characteristic of clinical data is their heterogeneity—such data include structured examination records and laboratory results, unstructured clinical notes, raw and tagged images, and genomic data. This heterogeneity poses a formidable challenge while constructing diagnostic and therapeutic decision models that are currently based on single modalities and are not able to use data in different formats and structures. This limitation may be addressed using data fusion methods. In this paper, we describe a case study where we aimed at developing data fusion models that resulted in various therapeutic decision models for predicting the type of treatment (surgical vs. non-surgical) for patients with bone fractures. We considered six different approaches to integrate clinical data: one fusion model based on combination of data (COD) and five models based on combination of interpretation (COI). Experimental results showed that the decision model constructed following COI fusion models is more accurate than decision models employing COD. Moreover, statistical analysis using the one-way ANOVA test revealed that there were two groups of constructed decision models, each containing the set of three different models. The results highlighted that the behavior of models within a group can be similar, although it may vary between different groups.
EN
Land use/land cover (LULC) maps are important datasets in various environmental projects. Our aim was to demonstrate how GEOBIA framework can be used for integrating different data sources and classification methods in context of LULC mapping.We presented multi-stage semi-automated GEOBIA classification workflow created for LULC mapping of Tuszyma Forestry Management area based on multi-source, multi-temporal and multi-resolution input data, such as 4 bands- aerial orthophoto, LiDAR-derived nDSM, Sentinel-2 multispectral satellite images and ancillary vector data. Various classification methods were applied, i.e. rule-based and Random Forest supervised classification. This approach allowed us to focus on classification of each class ‘individually’ by taking advantage from all useful information from various input data, expert knowledge, and advanced machine-learning tools. In the first step, twelve classes were assigned in two-steps rule-based classification approach either vector-based, ortho- and vector-based or orthoand Lidar-based. Then, supervised classification was performed with use of Random Forest algorithm. Three agriculture-related LULC classes with vegetation alternating conditions were assigned based on aerial orthophoto and Sentinel-2 information. For classification of 15 LULC classes we obtained 81.3% overall accuracy and kappa coefficient of 0.78. The visual evaluation and class coverage comparison showed that the generated LULC layer differs from the existing land cover maps especially in relative cover of agriculture-related classes. Generally, the created map can be considered as superior to the existing data in terms of the level of details and correspondence to actual environmental and vegetation conditions that can be observed in RS images.
9
Content available Fast multispectral deep fusion networks
EN
Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems.
EN
Due to safety reasons, the movement of a ship in coastal areas should be monitored, tracked, recorded, and stored. The Automatic Identification System (AIS) is a suitable tool to use in performing these functions. The probability limit for the AIS dynamic data availability can be limited by the lack of a Global Position System (GPS) signal, heading (HDG), and rate of turn (ROT) data in the position report. The unavailability of a data link is an additional limitation. To fill this gap, it is possible to attach the discrete Kalman filter (KF) for the position and course estimation. Coordinate estimation in the absence of a transmission link can improve the quality of the AIS service at Vessel Traffic Service (VTS) stations. This paper has presented the Kalman filtering algorithm to improve the possibilities for ship motion tracking and monitoring in the TSS (Traffic Separation Scheme) and fairways area. More than 570 iterations were calculated and the results have been presented in figures to familiarize the reader with the operating principle of the Kalman filter algorithm.
11
EN
Earth surface monitoring can give information that may be used in complex analysis of the air conditions, temperature, humidity etc. Data from a vertical profile of the atmosphere is also essential for accurate thunderstorm forecasting. That data is collected by radiosondes – telemetry instruments carried into the atmosphere usually by balloons. Sometimes, due to the hostile conditions of upper troposphere, incorrect data can be generated by radiosonde sensors. In this paper, a new algorithm is developed for fixing the incorrect data, i.e. missing or out of specific range values. The proposed algorithm was tested both on benchmarks and real data generated by radiosondes. About 70% of significantly damaged test data volume was recovered. Up to 100% of real data was fixed.
EN
Navigation system is one of the most important aircraft systems. Accuracy and precision of position and attitude is extremely important for safe aircraft operations. The integrated INS/GNSS systems are commonly used as autonomous on-board devices for fulfilling this task. The INS sensors like accelerometers and gyroscopes are mainly affected by drift. The GNSS encounter stochastic disturbances with no tendency to grow in time but as each radio navigation system may be jammed or its signal can just be not available. These base properties of errors make these two systems well suited for integration. These were the main motivations for development of integrated navigation and attitude determination system, presented in this article. In the developed system, data is integrated from all available sensors, particularly INS, GPS, and air data computer. Navigation information from these sensors is combined using Kalman filtering algorithms to obtain robust solution, effective also in a case of failure/inaccessibility of GPS. Position calculated using the accelerations from INS is corrected by position from GPS and optionally by position calculated using the true airspeed (TAS) from ADC. Navigation system is modelled and programmed in MATLAB environment. The system was tested using the data from real experiments, proving efficiency of the method.
PL
W niniejszym artykule prezentowana jest technika wykrywania i oceny wad w materiałach kompozytowych, bazująca na integracji aktywnej termografii i metody terahercowej. W badaniach eksperymentalnych została wykorzystana próbka wykonana z kompozytu szklanego z szeregiem nawierceń symulujących rzeczywiste wady. W związku z faktem, iż aktywna termografia i technika terahercowa są wrażliwe na zmiany różnych parametrów fizycznych materiału, połączenie wyników uzyskanych za pomocą obydwu metod pozwoli na otrzymanie możliwie pełnej informacji o badanej próbce.
EN
In this paper the technique of defects evaluation in composite materials based on a combination of active thermography method and terahertz technique is presented. The composite glass fiber sample with arifitial cirindlical defects having different depths was chosen for the tests. Active Infrared thermography with halogen lamps and terahertz technique are sensitive to changes in different physical parameters of the tested material. The combination of these techniques, performed by the data fusion of obtained measurements results allows to obtain the more complete on the detected material’s defect.
PL
Skanowanie wyrobów kształtowanych w technologii przyrostowej na bazie materiałów polimerowych wymaga odtworzenia pełnej geometrii z zachowaniem tekstury i kolorów. W artykule rozważono możliwość zastosowania fuzji danych pozwalającej na łączenie informacji pochodzącej z dwóch przyrządów pomiarowych, różniących się charakterystyką metrologiczną, właściwościami i specyfiką pracy. Przedstawiono fragment badań mających na celu ocenę wpływu wybranych czynników zewnętrznych kształtujących warunki pracy konkretnych modeli skanerów na jakość otrzymanych wyników. Wnioski z przeprowadzonych badań będą mogły być uwzględnione podczas dokonywania pomiarów tymi przyrządami, w celu wypracowania odpowiednio korzystnych wyników fuzji.
EN
Scanning the parts manufactured additive technique based on polymer materials requires to recreate the full geometry while maintaining the texture and colors. In the article analyzed the possibility of using data fusion technology that allows to combine Information coming from two measuring Instruments with metrological different characteristics, different properties, as well different specification of working. The authors show a part of research on the influence of some external factors forming the working conditions of concrete scanner models on the quality of their results. It is assumed that the conclusions of this study can be taken into account while measuring of these Instruments, In order to develop the favorable results of the data fusion.
EN
This paper presents the work completed under a research project titled „Design of a mobile platform for the support of forensic testing of scenes with potential CBRN hazards”. The study focuses on operation of the mobile platform control algorithm, determination of the mobile platform position and preparation of the mobile platform system for integration with a video navigation system. The sensors installed on the mobile platform are intended as emergency backup systems in the event of loss of communication between the platform and its operator. The results of the test drive sessions completed to verify the control algorithm performance are also given.
PL
Artykuł prezentuje prace wykonane w ramach projektu badawczego „Zaprojektowanie mobilnej platformy do wsparcia badań kryminalistycznych miejsc zdarzeń, w których może występować zagrożenie CBRN”. W artykule przedstawiono sposób działania algorytmu sterowania platformą, wyznaczania pozycji oraz przygotowanie systemu do integracji z nawigacją wizyjną. Czujniki, jakie zostały zamontowane na platformie, mają służyć za systemy awaryjne w razie utraty łączności pomiędzy platformą a operatorem. W artykule przedstawione zostały wyniki z przejazdów próbnych weryfikujących działanie algorytmu.
PL
Praca dotyczy zagadnienia identyfikacji danych głębi z danymi intensywności w zastosowaniu do zadania jednoczesnej samolokalizacji i budowy mapy otoczenia (ang. Simultaneous Localization and Mapping – SLAM). Integracja danych przeprowadzona została dla czujnika głębi o rozdzielczości obrazu znacząco mniejszej od rozdzielczości obrazu RGB. W artykule sprawdzono dwie metody zwiększania rozdzielczości. Jedna z nich wykorzystuje interpolację biliniową obrazu głębi podczas jego przeskalowywania, a druga opiera się na fuzji danych głębi oraz intensywności. Przetestowano jakość działanie obu metod podwyższania rozdzielczości w zadaniu SLAM. Porównanie podejść nastąpiło na podstawie eksperymentu, w którym zebrano dane z dwóch czujników głębi wraz z kamerą RGB oraz zarejestrowano trajektorię przemieszczenia czujników.
EN
In this paper the method of combining depth data with intensity images in the Simultaneous Localization and Mapping (SLAM) task is described. Data integration was performed for the depth sensor with substantially lower resolution than the RGB image. In this paper two methods of upsampling were tested. First one is using pure interpolation during upsampling, and the second one is based on data fusion guided by RGB image. The quality of selected methods was examined in a SLAM task. The comparison of these two methods was done experimentally, where data from depth and RGB sensors were gathered and the trajectory of the sensor movement was recorded.
EN
In this paper, we discuss a software architecture, which has been developed for the needs of the System for Intelligent Maritime Monitoring (SIMMO). The system bases on the state-of-the-art information fusion and intelligence analysis techniques, which generates an enhanced Recognized Maritime Picture and thus supports situation analysis and decision-making. The SIMMO system aims to automatically fuse an up-to-date maritime data from Automatic Identification System (AIS) and open Internet sources. Based on collected data, data analysis is performed to detect suspicious vessels. Functionality of the system is realized in a number of different modules (web crawlers, data fusion, anomaly detection, visualization modules) that share the AIS and external data stored in the system’s database. The aim of this article is to demonstrate how external information can be leveraged in maritime awareness system and what software solutions are necessary. A working system is presented as a proof of concept.
PL
Prezentowany artykuł omawia architekturę oprogramowania opracowanego na potrzeby projektu System for Intelligent Maritime Monitoring (SIMMO). System ten bazuje na najnowszych osiągnięciach w dziedzinach fuzji oraz inteligentnej analizy danych w celu generowania wzbogaconego obrazu sytuacji na morzu i wspomagania decyzji. SIMMO w sposób automatyczny łączy dane dotyczące ruchu morskiego z systemu AIS z danymi pochodzącymi z otwartych źródeł internetowych. Dzięki zebranym danym możliwa jest analiza w celu wykrycia podejrzanych zachowań na morzu. Funkcjonalność systemu stanowi wypadkową zawartych w nim modułów (ekstrakcja danych, fuzja danych, detekcja anomalii, wizualizacja) współdzielących dostęp do baz z danymi AIS oraz z zewnętrznych źródeł. Celem artykułu jest demonstracja sposobu wykorzystywania zewnętrznych informacji w systemach przeznaczonych do monitorowania ruchu morskiego, a także prezentacja działającego systemu.
18
Content available SLAM aided Inertial Navigation System
EN
The interdisciplinary nature of navigation leads us to drawing on knowledge contained in solutions used in related technical fields. An example of this trend is combining it with elements of robotics, in which SLAM (Simultaneous Localization And Mapping) is commonly used for positioning a vehicle. To calculate position changes, the location of characteristic objects on a continuously updated map of an environment is used. The attractiveness of the implementation of this technology in connection with marine navigational aids, stems from the possibility of enhancing positioning accuracy in harbor, off-shore or narrow areas. That is in the areas where there is a built up hydro-technical infrastructure, such as breakwaters, waterfronts or navigational infrastructure in the form of marked water fairways and anchorages. In this article an analysis of SLAM combined with INS (Inertial Navigation System) is carried out. It focuses on the possibilities of enhancing accuracy in fixing position coordinates for a submarine. The first part of the article presents a mathematical base for combining INS and SLAM using the Extended Kalman Filter. The second part describes a study on the accuracy in positioning a mobile robot (in this instance a wheeled vehicle) which employs a navigation system based on INS and INS aided SLAM. The final part of the article includes the results of the study and their analysis. It also contains generalized conclusions indicating advantages and disadvantages of the proposed solution.
PL
Interdyscyplinarność nawigacji skłania do czerpania wiedzy z rozwiązań stosowanych w pokrewnych dziedzinach nauk technicznych. Przykładem jest połączenie z elementami robotyki, w której do pozycjonowania pojazdu powszechnie wykorzystywana jest technika SLAM (Simultaneous Localization And Mapping). Polega ona na pozycjonowaniu pojazdu na podstawie zmian położenia obiektów charakterystycznych znajdujących się na stale aktualizowanej mapie otoczenia. Implementacja tej technologii w połączeniu z morskimi urządzeniami nawigacyjnymi zwiększa dokładność pozycjonowania w obszarach portowych, przybrzeżnych lub ścieśnionych, gdzie istnieje rozbudowana infrastruktura hydrotechniczna, np. falochrony, nabrzeża, oraz infrastruktura nawigacyjna w postaci oznakowanych torów wodnych i kotwicowisk. W artykule przeprowadzono analizę technologii SLAM w połączeniu z INS (Inertial Navigation System) pod kątem możliwości zwiększenia dokładności wypracowywania współrzędnych pozycji na okręcie podwodnym. W pierwszej części przedstawiono podstawę matematyczną zespolenia INS ze SLAM przy użyciu rozszerzonego filtru Kalmana (Extended Kalman Filter), w drugiej opisano badanie dokładności pozycjonowania robota mobilnego (pojazdu kołowego) wykorzystującego system nawigacyjny oparty na INS i INS wspomagany SLAM, na zakończenie przedstawiono wyniki badania oraz ich analizę, a także uogólnione wnioski ukazujące zalety i wady zaproponowanego rozwiązania.
19
Content available remote Bi-axial Neutral Axis Tracking for Crack Detection in Wind Turbine Towers
EN
This work concentrates on Structural Health Monitoring (SHM) of a wind turbine tower. The paper investigates the use of a decision level data fusion based on bi-axial tracking of change in the neutral axis (NA) position for damage detection in wind turbine towers. A discrete Kalman Filter (KF) is employed for the estimation of the NA in the presence of measurement noise from the strain sensors. The KF allows data fusion from the strain sensors and the yaw mechanism for the accurate estimation of the NA. Any change in the NA position may be used for detecting and locating the damage. The tan inverse of the ratio of the change in the NA along two perpendicular axes is taken and used for the localization. The study was carried out on a simulated finite element (FE) model of a wind turbine tower with a surface crack. The sensitivity studies carried out on the structure in terms of different crack sizes, crack locations and crack orientations indicate that the methodology is robust enough to detect the crack under different operational loading conditions.
EN
This paper presents the design process and the results of a novel fall detector designed and constructed at the Faculty of Electronics, Military University of Technology. High sensitivity and low false alarm rates were achieved by using four independent sensors of varying physical quantities and sophisticated methods of signal processing and data mining. The manuscript discusses the study background, hardware development, alternative algorithms used for the sensor data processing and fusion for identification of the most efficient solution and the final results from testing the Android application on smartphone. The test was performed in four 6-h sessions (two sessions with female participants at the age of 28 years, one session with male participants aged 28 years and one involving a man at the age of 49 years) and showed correct detection of all 40 simulated falls with only three false alarms. Our results confirmed the sensitivity of the proposed algorithm to be 100% with a nominal false alarm rate (one false alarm per 8 h).
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