76 research outputs found
Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Identifying specific locations of items such as containers, warehouse pellets, and returnable packages in a large environment, for instance, in a warehouse, requires an extensive tracking system that could identify the location through data visualization. This is the similar case for radio-frequency identification (RFID) pallet level signal as the accuracy of determining the position for specific location either on the level or stacked in the same direction are read uniformly. However, there is no single study focusing on pallet-level classification, in particular on distance measurement of pallet height. Hence, a methodological approach that could provide the solution is essential to reduce the misplaced issues and thus reduce the problem in searching the products in a large-scale setting. The objective of this work attempts to define the pallet level of the stacked RFID tags through the machine learning techniques framework. The methodology started with the pallet-level which firstly determined by manual clustering according to the product code number of the tags that were manufactured for defining the actual level. An additional study of the radio frequency of the tagged pallet box in static condition was carried out by determining the feature of the time series. Various sample sizes of 1 Hz, 5 Hz and 10 Hz combined with the received signal strength of maximum, minimum, mode, median, mean, variance, maximum and minimum difference, kurtosis and skewness are evaluated. The statistical features of the received signal strength reading are analyzed by the selection of the univariate features, feature importance technique, and principal component analysis. The received signal strength of the maximum, median, and mean of all statistical features has been shown to be significant specifically for the 10Hz sample size. Different machine learning classifiers were tested based on the significant features, namely the Artificial Neural Network, Decision Tree, Random Forest, Naive Bayes Support Vector Machine, and k-Nearest Neighbors. It was shown that up to 95.02% of the trained Random Forest Model could be classified, indicating that the established framework is viable for pallet classification. Furthermore, the efficacy of different models based on heuristic hyperparameter tuning is evaluated in which the different kernel function for Support Vector Machine, various distance metrics of k-Nearest Neighbors. The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. In results, it was found that the Random Forest provided 92.44% of the test sets with the highest accuracy. In order to further validate the position of the tagging in the pallet box of the Random Forest model developed, a different predefined location was used to validate the model. The best position that could achieve a classification accuracy of 93.30% through the validation process for position five (5) in the systematic model that is the centre of the pallet box. In conclusion, it can be inferred from the analysis that the Random Forest model has better predictive performance compared to the rest of the pallet level partition model with a height of 12 cm used in this research. Based on the train, validation, and test sets in Random Forest, the RFID capability to determine the position of the pallet can be detected precisely
PDR with a Foot-Mounted IMU and Ramp Detection
The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the personâs body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the personâs foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps
INDOOR-WIRELESS LOCATION TECHNIQUES AND ALGORITHMS UTILIZING UHF RFID AND BLE TECHNOLOGIES
The work presented herein explores the ability of Ultra High Frequency Radio Frequency (UHF RF) devices, specifically (Radio Frequency Identification) RFID passive tags and Bluetooth Low Energy (BLE) to be used as tools to locate items of interest inside a building. Localization Systems based on these technologies are commercially available, but have failed to be widely adopted due to significant drawbacks in the accuracy and reliability of state of the art systems. It is the goal of this work to address that issue by identifying and potentially improving upon localization algorithms.
The work presented here breaks the process of localization into distance estimations and trilateration algorithms to use those estimations to determine a 2D location. Distance estimations are the largest error source in trilateration. Several methods are proposed to improve speed and accuracy of measurements using additional information from frequency variations and phase angle information. Adding information from the characteristic signature of multipath signals allowed for a significant reduction in distance estimation error for both BLE and RFID which was quantified using neural network optimization techniques. The resulting error reduction algorithm was generalizable to completely new environments with very different multipath behavior and was a significant contribution of this work.
Another significant contribution of this work is the experimental comparison of trilateration algorithms, which tested new and existing methods of trilateration for accuracy in a controlled environment using the same data sets. Several new or improved methods of triangulation are presented as well as traditional methods from the literature in the analysis. The Antenna Pattern Method represents a new way of compensating for the antenna radiation pattern and its potential impact on signal strength, which is also an important contribution of this effort. The performance of each algorithm for multiple types of inputs are compared and the resulting error matrix allows a potential system designer to select the best option given the particular system constraints
Using Distributed Wearable Sensors to Measure and Evaluate Human Lower Limb Motions
This paper presents a wearable sensor approach to motion measurements of human lower limbs, in which subjects perform specified walking trials at self-administered speeds so that their level walking and stair ascent capacity can be effectively evaluated. After an initial sensor alignment with the reduced error, quaternion is used to represent 3-D orientation and an optimized gradient descent algorithm is deployed to calculate the quaternion derivative. Sensors on the shank offer additional information to accurately determine the instances of both swing and stance phases. The Denavit-Hartenberg convention is used to set up the kinematic chains when the foot stays stationary on the ground, producing state constraints to minimize the estimation error of knee position. The reliability of this system, from the measurement point of view, has been validated by means of the results obtained from a commercial motion tracking system, namely, Vicon, on healthy subjects. The step size error and the position estimation accuracy change are studied. The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved. The proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
Indoor Mapping and Reconstruction with Mobile Augmented Reality Sensor Systems
Augmented Reality (AR) ermöglicht es, virtuelle, dreidimensionale Inhalte direkt
innerhalb der realen Umgebung darzustellen. Anstatt jedoch beliebige virtuelle
Objekte an einem willkĂŒrlichen Ort anzuzeigen, kann AR Technologie auch genutzt
werden, um Geodaten in situ an jenem Ort darzustellen, auf den sich die Daten
beziehen. Damit eröffnet AR die Möglichkeit, die reale Welt durch virtuelle, ortbezogene
Informationen anzureichern. Im Rahmen der vorliegenen Arbeit wird diese
Spielart von AR als "Fused Reality" definiert und eingehend diskutiert.
Der praktische Mehrwert, den dieses Konzept der Fused Reality bietet, lÀsst sich
gut am Beispiel seiner Anwendung im Zusammenhang mit digitalen GebÀudemodellen
demonstrieren, wo sich gebÀudespezifische Informationen - beispielsweise der
Verlauf von Leitungen und Kabeln innerhalb der WĂ€nde - lagegerecht am realen
Objekt darstellen lassen. Um das skizzierte Konzept einer Indoor Fused Reality
Anwendung realisieren zu können, mĂŒssen einige grundlegende Bedingungen erfĂŒllt
sein. So kann ein bestimmtes GebÀude nur dann mit ortsbezogenen Informationen
augmentiert werden, wenn von diesem GebĂ€ude ein digitales Modell verfĂŒgbar ist.
Zwar werden gröĂere Bauprojekt heutzutage oft unter Zuhilfename von Building
Information Modelling (BIM) geplant und durchgefĂŒhrt, sodass ein digitales Modell
direkt zusammen mit dem realen GebÀude ensteht, jedoch sind im Falle Àlterer
BestandsgebĂ€ude digitale Modelle meist nicht verfĂŒgbar. Ein digitales Modell eines
bestehenden GebĂ€udes manuell zu erstellen, ist zwar möglich, jedoch mit groĂem
Aufwand verbunden. Ist ein passendes GebÀudemodell vorhanden, muss ein AR
GerĂ€t auĂerdem in der Lage sein, die eigene Position und Orientierung im GebĂ€ude
relativ zu diesem Modell bestimmen zu können, um Augmentierungen lagegerecht
anzeigen zu können.
Im Rahmen dieser Arbeit werden diverse Aspekte der angesprochenen Problematik
untersucht und diskutiert. Dabei werden zunÀchst verschiedene Möglichkeiten
diskutiert, Indoor-GebĂ€udegeometrie mittels Sensorsystemen zu erfassen. AnschlieĂend
wird eine Untersuchung prÀsentiert, inwiefern moderne AR GerÀte, die
in der Regel ebenfalls ĂŒber eine Vielzahl an Sensoren verfĂŒgen, ebenfalls geeignet
sind, als Indoor-Mapping-Systeme eingesetzt zu werden. Die resultierenden Indoor
Mapping DatensÀtze können daraufhin genutzt werden, um automatisiert
GebÀudemodelle zu rekonstruieren. Zu diesem Zweck wird ein automatisiertes,
voxel-basiertes Indoor-Rekonstruktionsverfahren vorgestellt. Dieses wird auĂerdem
auf der Grundlage vierer zu diesem Zweck erfasster DatensÀtze mit zugehörigen
Referenzdaten quantitativ evaluiert. Desweiteren werden verschiedene
Möglichkeiten diskutiert, mobile AR GerÀte innerhalb eines GebÀudes und des zugehörigen
GebĂ€udemodells zu lokalisieren. In diesem Kontext wird auĂerdem auch
die Evaluierung einer Marker-basierten Indoor-Lokalisierungsmethode prÀsentiert.
AbschlieĂend wird zudem ein neuer Ansatz, Indoor-Mapping DatensĂ€tze an den
Achsen des Koordinatensystems auszurichten, vorgestellt
Experimental study on location tracking of construction resources using UWB for better productivity and safety
There is a growing demand for accurate and up-to-date information in the construction industry. Ultra-Wideband (UWB) Real-Time Location Systems (RTLSs) enable tracking and visualization of resources on site and give more awareness to the construction staff in near real time. This research investigates how UWB technology can improve productivity and safety in construction projects. The requirements of the RTLSs are identified in terms of safety and productivity management. The usability of RTLSs in the construction industry is tested by the collection of data from a construction site and organizing them into useful information needed for management. It was found that UWB is an effective tool to monitor construction resources because it provides accurate information in near real time. However, good understanding of the requirements and filtering the data are necessary in order to get the best benefit of the technology for productivity and safety purposes
Latitude, longitude, and beyond:mining mobile objects' behavior
Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity
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