Assessment of the Integration Strategy between GPS and Body-Worn MEMS Sensors with Application to Sports

This paper describes experiments that were performed involving a professional downhill skier equipped with a low-cost L1 GPS receiver and a MEMS-IMU composed of 3 single axis gyroscopes, accelerometers and magnetometers. In addition, the skier carried an L1/L2 GPS receiver and a tactical-grade IMU (LN200). The experiments aimed to assess the navigation performance of different GPS/MEMS-IMU integration strategies compared to high-quality GPS/INS integration. After presenting an overview of currently applied integration methods, the unscented Kalman filter approach in loosely coupled mode. The relevance of the simple MEMS-IMU sensor error model was verified by comparing the filter output to the reference data

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Most existing ESC (Electronic Stability Control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in the relevant literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. In this paper, we propose a novel observer based on ANFIS, combined with Kalman Filters in order to estimate the sideslip angle, which in turn is used to control the vehicle dynamics and improve its behavior. For this reason, low-cost sensor measurements which are integrated into the actual vehicle and executed in real time have to be used. The ANFIS system estimates a "pseudo-sideslip angle" through parameters which are easily measured, using sensors equipped in actual vehicles (inertial sensors and steering wheel sensors); this value is introduced in UKF in order to filter noise and to minimize the variance of the estimation mean square error. The estimator has been validated by comparing the observed proposal with the values provided by the CARSIM model, which is a piece of experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle, by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS+UKF-based sideslip angle estimator

A Wireless Sensor Network Based Personnel Positioning Scheme in Coal Mines with Blind Areas

This paper proposes a novel personnel positioning scheme for a tunnel network with blind areas, which compared with most existing schemes offers both low-cost and high-precision. Based on the data models of tunnel networks, measurement networks and mobile miners, the global positioning method is divided into four steps: (1) calculate the real time personnel location in local areas using a location engine, and send it to the upper computer through the gateway; (2) correct any localization errors resulting from the underground tunnel environmental interference; (3) determine the global three-dimensional position by coordinate transformation; (4) estimate the personnel locations in the blind areas. A prototype system constructed to verify the positioning performance shows that the proposed positioning system has good reliability, scalability, and positioning performance. In particular, the static localization error of the positioning system is less than 2.4 m in the underground tunnel environment and the moving estimation error is below 4.5 m in the corridor environment. The system was operated continuously over three months without any failures

A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

In providing acceptable navigational solutions, Location-Based Services (LBS) in land navigation rely mostly on integration of Global Positioning System (GPS) and Inertial Navigation System (INS) measurements for accuracy and robustness. The GPS/INS integrated system can provide better land-navigation solutions than the ones any standalone system can provide. Low-cost Inertial Measurement Units (IMUs), based on Microelectromechanical Systems (MEMS) technology, revolutionized the land-navigation system by virtue of their low-cost miniaturization and widespread availability. However, their accuracy is strongly affected by their inherent systematic and stochastic errors, which depend mainly on environmental conditions. The environmental noise and nonlinearities prevent obtaining optimal localization estimates in Land Vehicular Navigation (LVN) while using traditional Kalman Filters (KF). The main goal of this paper is to effectively eliminate stochastic errors of MEMS-based IMUs. The proposed solution is divided into two main components: (1) improving noise cancellation, using advanced stochastic error models in MEMS-based IMUs based on combined Autoregressive Processes (ARP) and first-order Gauss-Markov Process (1GMP), and (2) modeling the low-cost GPS/INS integration, using a hybrid Fuzzy Inference System (FIS) and Second-Order Extended Kalman Filter (SOEKF). The results obtained show that the proposed methods perform better than the traditional techniques do in different stochastic and dynamic situations

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve the navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence - the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms - that have been shown able to improve the accuracy of current positioning estimation systems for real-time UAV autonomous navigation. In addition, the proposed methodology outperformed two other Computational Intelligence techniques

Object Detection and Recognition for Visually Impaired People

Object detection plays a very important role in many applications such as image retrieval, surveillance, robot navigation, wayfinding, etc. In this thesis, we propose different approaches to detect indoor signage, stairs and pedestrians. In the first chapter we introduce some related work in this field. In the second chapter, we introduced a new method to detect the indoor signage to help blind people find their destination in unfamiliar environments. Our method first extracts the attended areas by using a saliency map. Then the signage is detected in the attended areas by using bipartite graph matching. The proposed method can handle multiple signage detection. Experimental results on our collected indoor signage dataset demonstrate the effectiveness and efficiency of our proposed method. Furthermore, saliency maps could eliminate the interference information and improve the accuracy of the detection results. In the third chapter, we present a novel camera-based approach to automatically detect and recognize restroom signage from surrounding environments. Our method first extracts the attended areas which may content signage based on shape detection. Then, Scale-Invariant Feature Transform (SIFT) is applied to extract local features in the detected attended areas. Finally, signage is detected and recognized as the regions with the SIFT matching scores larger than a threshold. The proposed method can handle multiple signage detection. Experimental results on our collected restroom signage dataset demonstrate the effectiveness and efficiency of our proposed method. In the fourth chapter, we develop a new framework to detect and recognize stairs and pedestrian crosswalks using a RGBD camera. Since both stairs and pedestrian crosswalks are featured by a group of parallel lines, we first apply Hough transform to extract the concurrent parallel lines based on the RGB channels. Then, the Depth channel is employed to further recognize pedestrian crosswalks, upstairs, and downstairs using support vector machine (SVM) classifiers. Furthermore, we estimate the distance between the camera and stairs for the blind users. The detection and recognition results on our collected dataset demonstrate that the effectiveness and efficiency of our proposed framework Keywords: Blind people, Navigation and wayfinding, Camera, Signage detection and recognition, Independent trave

Yapay zeka teknikleri kullanılarak öğrenci danışmanlık hizmeti bilişim altyapı tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Üniversiteler, öğrencilerin kendi alanlarının farklı dallarında kendilerini geliştirebilmeleri için seçmeli dersler sunarlar. Temel dersleri alan bir öğrenci ders seçim haftalarında bu seçmeli dersler için bir seçim kararı verir. Her öğrencinin bu karardan beklentileri farklıdır. Farklı beklentilerin ortak paydası olarak dersten alınacak başarı puanı gösterilebilir. Öğrencilerin beklentilerinin karşılanmasında ve başarılı olabilecekleri bir alana yönlendirilmesinde danışmanlara büyük sorumluluk düşmektedir. Danışmanlık hizmeti öğrencilerin doğru yönlendirilebilmesi için önemli bir olgudur ve verim alınabilmesi için danışman öğrenci ilişkisinin kuvvetli olması gerekir. Fakat danışmanların ağır iş yüklerinden, öğrencilerin ilgi alanlarını keşfetmek ve öğrencileri tanımak için zamanın kısıtlı olması, öğrencilerin farklı şehirlerde ikamet etmeleri gibi nedenlerden dolayı öğrencilerle danışmanları arasındaki olması gereken ilişki sağlanamamaktadır. Bu çalışmada geçmiş öğrenci verilerinden faydalanılarak dönem sonunda öğrencilerin seçmeli dersleri hangi başarı puanı ile tamamlayacağının tahmini yapılmaktadır. Bu çalışmayla öğrencilere danışmanlık hizmeti, danışman ve öğreticilere ise bir karar destek sistemi sunulmaktadır. Çalışma öğrencilere ders seçim haftalarında bir rehberlik sağlar ve öğrencilerin danışmana olan bağımlılıklarını azaltır. Danışmanlara öğrencisinin yeteneklerini, ilgi alanlarını keşfetmesinde ve öğrencilerini kısa zamanda daha iyi tanımasında yardım eder. Dersi veren öğreticiler, daha önceden sınıfın bilgi düzeyini ölçebilir gerekirse dersiyle ilgili iyileştirmeleri yapabilir, ders içeriğini ve ders akışını öğrenci profiline göre düzenleyebilir. Üniversite yönetimine yeni dönem için yapılacak planlamalarında fayda sağlar. Var olan öğrenci potansiyelleri önceden görülebilir ve derslerin birbirleriyle ilişkisi öğrenilebilir.Universities offer elective cources to students in order to develop themselves in their different fields. Students who have completed basic courses, makes a choice decision for elective courses in course selection weeks. Each student's expectations from that decision are different. Success point of the course can be shown as the common denominator of different expectations. Consultants have a great responsibility for students in order to satisfy their expectations and directing them to a field that they will able to be successful. Consulting service is an important phenomenon. To achieve efficiency, counselor and student relationship need to be strong. However, the reasons such as the heavy workload of advisers, lack of time to get to know the them and explore their interests, the students residing in different cities, it is not possible to provide a relationship that need to be between student and counselor. In this research, the success of a student at the end of semester from elective courses which he/she wants to choose, is estimated by benefiting previous students data. With this study, counseling services to students and a decision support system to counselor and trainers has been presented. The study provides guidance to students in course selection weeks and reduce students' dependence on consultants. This study provides support to advisor in estimating success of a student in course and provides help in discovering talents of his/her students and better understanding them. Lecturers can observe the capacity of class previously, and make improvements about course, reorganise syllabus and course flow. It helps to the university management for planning the new semesters. Students' potantial can be seen before and related lessons can be learned

Trajectory determination and analysis in sports by satellite and inertial navigation

This research presents methods for performance analysis in sports through the integration of Global Positioning System (GPS) measurements with Inertial Navigation System (INS). The described approach focuses on strapdown inertial navigation using Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU). A simple inertial error model is proposed and its relevance is proven by comparison to reference data. The concept is then extended to a setup employing several MEMS-IMUs in parallel. The performance of the system is validated with experiments in skiing and motorcycling. The position accuracy achieved with the integrated system varies from decimeter level with dual-frequency differential GPS (DGPS) to 0.7 m for low-cost, single-frequency DGPS. Unlike the position, the velocity accuracy (0.2 m/s) and orientation accuracy (1 – 2 deg) are almost insensitive to the choice of the receiver hardware. The orientation performance, however, is improved by 30 – 50% when integrating four MEMS-IMUs in skew-redundant configuration. Later part of this research introduces a methodology for trajectory comparison. It is shown that trajectories based on dual-frequency GPS positions can be directly modeled and compared using cubic spline smoothing, while those derived from single-frequency DGPS require additional filtering and matching