Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris

Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai 325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap. Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam membuat kajian yang akan datang

Pedestrian localisation for indoor environments

Ubiquitous computing systems aim to assist us as we go about our daily lives, whilst at the same time fading into the background so that we do not notice their presence. To do this they need to be able to sense their surroundings and infer context about the state of the world. Location has proven to be an important source of contextual information for such systems. If a device can determine its own location then it can infer its surroundings and adapt accordingly. Of particular interest for many ubiquitous computing systems is the ability to track people in indoor environments. This interest has led to the development of many indoor location systems based on a range of technologies including infra-red light, ultrasound and radio. Unfortunately existing systems that achieve the kind of sub-metre accuracies desired by many location-aware applications require large amounts of infrastructure to be installed into the environment. This thesis investigates an alternative approach to indoor pedestrian tracking that uses on-body inertial sensors rather than relying on fixed infrastructure. It is demonstrated that general purpose inertial navigation algorithms are unsuitable for pedestrian tracking due to the rapid accumulation of errors in the tracked position. In practice it is necessary to frequently correct such algorithms using additional measurements or constraints. An extended Kalman filter is developed for this purpose and is applied to track pedestrians using foot-mounted inertial sensors. By detecting when the foot is stationary and applying zero velocity corrections a pedestrian’s relative movements can be tracked far more accurately than is possible using uncorrected inertial navigation. Having developed an effective means of calculating a pedestrian’s relative movements, a localisation filter is developed that combines relative movement measurements with environmental constraints derived from a map of the environment. By enforcing constraints such as impassable walls and floors the filter is able to narrow down the absolute position of a pedestrian as they move through an indoor environment. Once the user’s position has been uniquely determined the same filter is demonstrated to track the user’s absolute position to sub-metre accuracy. The localisation filter in its simplest form is computationally expensive. Furthermore symmetry exhibited by the environment may delay or prevent the filter from determining the user’s position. The final part of this thesis describes the concept of assisted localisation, in which additional measurements are used to solve both of these problems. The use of sparsely deployed WiFi access points is discussed in detail. The thesis concludes that inertial sensors can be used to track pedestrians in indoor environments. Such an approach is suited to cases in which it is impossible or impractical to install large amounts of fixed infrastructure into the environment in advance

Map matching by using inertial sensors: literature review

This literature review aims to clarify what is known about map matching by using inertial sensors and what are the requirements for map matching, inertial sensors, placement and possible complementary position technology. The target is to develop a wearable location system that can position itself within a complex construction environment automatically with the aid of an accurate building model. The wearable location system should work on a tablet computer which is running an augmented reality (AR) solution and is capable of track and visualize 3D-CAD models in real environment. The wearable location system is needed to support the system in initialization of the accurate camera pose calculation and automatically finding the right location in the 3D-CAD model. One type of sensor which does seem applicable to people tracking is inertial measurement unit (IMU). The IMU sensors in aerospace applications, based on laser based gyroscopes, are big but provide a very accurate position estimation with a limited drift. Small and light units such as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very popular, but they have a significant bias and therefore suffer from large drifts and require method for calibration like map matching. The system requires very little fixed infrastructure, the monetary cost is proportional to the number of users, rather than to the coverage area as is the case for traditional absolute indoor location systems.Siirretty Doriast

Floor plan-free particle filter for indoor positioning of industrial vehicles

Industry 4.0 is triggering the rapid development of solutions for indoor localization of industrial ve- hicles in the factories of the future. Either to support indoor navigation or to improve the operations of the factory, the localization of industrial vehicles imposes demanding requirements such as high accuracy, coverage of the entire operating area, low convergence time and high reliability. Industrial vehicles can be located using Wi-Fi fingerprinting, although with large positioning errors. In addition, these vehicles may be tracked with motion sensors, however an initial position is necessary and these sensors often suffer from cumulative errors (e.g. drift in the heading). To overcome these problems, we propose an indoor positioning system (IPS) based on a particle filter that combines Wi-Fi fingerprinting with data from motion sensors (displacement and heading). Wi-Fi position estimates are obtained using a novel approach, which explores signal strength measurements from multiple Wi-Fi interfaces. This IPS is capable of locating a vehicle prototype without prior knowledge of the starting position and heading, without depending on the building’s floor plan. An average positioning error of 0.74 m was achieved in performed tests in a factory-like building.FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the PhD fellowship PD/BD/137401/2018 and the Technological Development in the scope of the projects in co-promotion no 002814/2015 (iFACTORY 2015-2018

Indoor Intruder Tracking Using Visible Light Communications

This paper proposes a comprehensive study of indoor intruder tracking using visible light communication (VLC). A realistic indoor VLC channel was developed, taking into consideration reflections, shadowing, and ambient noise. The intruder was considered smart and aiming to escape tracking. This was modelled by adding noise and disturbance to the intruder’s trajectory. We propose to extend the application of minimax filtering from state estimation in the radio frequency (RF) domain to intruder tracking using VLC. The performance of the proposed method was examined and compared with Kalman filter for both VLC and RF. The simulation results showed that the minimax filter provided marginally better tracking and was more robust to the adversary behavior of the intruder than Kalman filter, with less than 0.5 cm estimation error. In addition, minimax was significantly better than Kalman filter for RF tracking applications

Filtering Impulses in Dynamic Noise in the Presence of Large Measurement Noise

This work considers the problem of filtering a system in which the dynamic noise occasionally has an impulse value that is an order of magnitude or more larger than its typical expected distribu-tion. This is particularly challenging when the ratio of measurement noise to typical dynamic noise is large enough that the impulse dynamic noise cannot be easily distinguished from a large random occurrence of measurement noise. A new filter model is proposed using a multiple model approach in which one of the models is an impulse. The implementation of the model is demonstrated in a Kalman filter framework. Simulation results show the improvement of the new filter over existing methods across a range of measurement, typical, and impulse dynamic noises. The filter is then ap-plied to three different problems: 2D human motion tracking using ultra-wideband (UWB) position measurements, power system state estimation on a coupled bus, and handling outlier measurement noise in UWB tracking. In each case the new filter demonstrates a 2-4% improvement over existing state-of-the-art techniques

A Study of Environment Noise in Ultra-Wideband Indoor Position Tracking

This work is motivated by the problem of improving the accuracy of indoor ultra-wideband (UWB) position tracking through the study of the environment noise that affects such a system. Current systems can provide accuracy in the range of 30-100 cm in a small building, suitable for applications that require rough room-level precision such as asset tracking and surveillance. Our long-term goal is to improve the accuracy to 1 cm or better, expanding potential applications to telepresence, augmented reality, training and entertainment. This work investigates the possibility of systematically observing the measurement noise of an UWB position tracking system and building a map of it throughout a facility. In order to understand the effect of environment noise on UWB indoor positioning and in turn filter out the effects of this noise, it is important to have an idea of what this measurement noise looks like in a real world scenario. In this work, an understanding of the measurement noise is gained by taking many measurements using a commercially-available UWB positioning system installed in a real world scenario and analyzing these measurements in various ways. To the author\u27s knowledge, no one has used such an exhaustive approach to analyze measurement noise in UWB indoor positioning. The results of this work show that the measurement noise that affects a UWB indoor position tracking system can be effectively modeled using a weighted sum of Gaussians, is stable over time and is locally similar. Furthermore, a particle filter augmented with a measurement noise map is proposed to improve position tracking accuracy. Finally, a metric is proposed that can be used to quantify expected system performance based on sensor location, sensor orientation and facility floorplan. Using this metric, a procedure is developed to determine the parameters, i.e. sensor position, sensor orientation and potentially others, of the physical installation of the UWB tracking system that will produce minimum measurement error based on sensor geometry and physical facility constraints