Automatic identification system is a prominent technology used in warehouses to give managers real time information of their products and assistance to warehouse employees in keeping an accurate inventory record. This kind of assistance is needed as an inaccurate inventory leads to profit loss due to misplacement or other mistakes. This project cooperated with an organization called Stora Enso specifically one of their forest nursery to find a solution to improve their inventory management system. Their current inventory system is a manual process which leads to mistakes occurring that affects the inventory accuracy. This thesis project evaluates automatic identification systems to observe if the technology is a possible solution and aims to answer the research question ”What are the significant impacts an automatic identification system has on an inventory management system?”. From the automatic identification evaluation one system is picked for further evaluation and due to its advantages radio frequency identification (RFID) is picked. To evaluate RFID in a warehouse setting a discrete-event simulation was created that simulates the forest nursery’s warehouse. The simulation is then used to evaluate the impact of different RFID implementations and their respective cost. The simulation results show that just a simple RFID implementation can improve inventory accuracy and remove some of the mistakes a manual system has with a relatively low direct cost. It also shows that a full RFID implementation that gives full visibility of a warehouse can almost remove inventory mistakes however the cost analysis shows that it requires a large investment.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2007. / SumbandilaSat is the second South African satellite, and is scheduled to be launched in April/May 2007. A software defined radio (SDR) automatic identification system (AIS) receiver is proposed as a possible experimental payload for this satellite. The AIS receiver can be used to track and store movement of ships at sea, and then forward this information to the ground station upon request. This thesis demonstrates the design of a SDR AIS receiver for Sumbandila satellite. The design of a GMSK/FMmodem as used in AIS is presented. Models are developed and simulated in Matlab. Digital signal processing algorithms developed for the AIS receiver are highlighted. Algorithms are developed to decode and translate the AIS encapsulated binary messages. The models are transferred to C++ and the AIS receiver is implemented on the SDR architecture. Finally the real time performance of the AIS receiver is presented along with some test results and performance analysis.

Levels of underwater noise in the open ocean have been increasing since at least the 1960s due to growth in global shipping traffic and the speed and propulsion power of vessels. This rise in noise levels reduces the range over which vocal marine species can communicate, and can induce physiological stress and behavioural responses, which may ultimately have population-level consequences. Although long-term noise trends have been studied at some open-ocean sites, in shallower coastal regions the high spatiotemporal variability of noise levels presents a substantial methodological challenge, and trends in these areas are poorly understood. This thesis addresses this challenge by introducing new techniques which combine multiple data sources for ship noise assessment in coastal waters. These data include Automatic Identification System (AIS) ship-tracking data, shore-based time-lapse footage, meteorological data, and tidal data. Two studies are presented: in the first, AIS data and acoustic recordings from Falmouth Bay in the western English Channel are combined using an adaptive threshold, which separates ship passages from background noise in the acoustic data. These passages are then cross-referenced with AIS vessel tracks, and the noise exposure associated with shipping activity is then determined. The second study, at a site in the Moray Firth, Scotland, expanded the method to include shore-based time-lapse footage, which enables visual corroboration of vessel identifications and the production of videos integrating the various data sources. Two further studies examine and enhance basic analysis techniques for ambient noise monitoring. The first study examines averaging metrics and their applicability to the assessment of noise from shipping. Long-term data from the VENUS observatory are empirically assessed for different averaging times and in the presence of outliers. It is concluded that the mean sound pressure level averaged in linear space is most appropriate, in terms of both standardization and relevance to impacts on marine fauna. In the second study, a new technique for the statistical analysis of long-term passive acoustic datasets, termed spectral probability density (SPD), is introduced. It is shown that the SPD can reveal characteristics such as multimodality, outlier influence, and persistent self-noise, which are not apparent using conventional techniques. This helps to interpret long-term datasets, and can indicate whether an instrument’s dynamic range is appropriate to field conditions. Taken together, the contributions presented in this thesis help to establish a stronger methodological basis for the assessment of shipping noise. These methods can help to inform emerging policy initiatives, efforts to standardise underwater noise measurements, and investigation into the effects of shipping noise on marine life.

<p>An ethnographic study loosely informed by the theoretical framework of distributed cognition was carried out in order to describe how mariners have adopted the Automatic Identification System (AIS) in their work practice, or"made the technology their own". AIS is a transponder-based identification and communication system that allows ships to automatically identify and track each other. In addition to facilitating the identification and tracking of ships, objectives behind the introduction of AIS are to"simplify informational exchange", and"provide additional information to assist situation awareness". Participant observation and interviews were made at four different ships, as well as at two shore stations. A focus group was also held at a maritime conference. The study gave some interesting results. For example, a Problem of Public Information Loss was identified. It is tentatively suggested that this problem has been overlooked partly because of a widespread but impoverished model of communication which does not account for the role of side-participants in a conversation. It is concluded that more research needs to be done on maritime work and the use of new bridge technology.</p>

Kaohsiung Harbor is one of the major commercial ports in Taiwan, located at the hub of northeastern and southeastern Asia shipping lanes. Therefore there are a considerable number of commercial shipping channels distributed around Kaohsiung Harbor. The security of Kaohsiung Harbor becomes more difficult to defense than others due to the complexity of channels. In this study, Automatic Identification System (AIS) system is used to collect the ships information from June 1, 2010 to June 30, 2011. The collected AIS data were decoded, converted, corrected, integrated and analyzed systematically, which will become the base of future database. The information of the AIS includes Maritime Mobile Service Identity (MMSI), latitude and longitude, heading, course, speed, and others. The activities of ships can be monitored by AIS, so the density and distribution of ships on each major channel can be obtained by grid computing. By the results of one-year AIS data, three major shipping channels of Kaohsiung Harbor can be identified, which are north-western, north-southern, and east-western. Based on this kind of long term shipping statistics, possible novel harbor security defense may be founded. Although the AIS was designed to monitor the ship activities, but it can be viciously shut down, or signal is out of range sometimes, then it will become the possible security breach. Nevertheless, ships at sea will generate certain kind of noises, such as from engine and propeller. With efficient propagation of sound waves in water, acoustic technology may compensate the limitations of AIS, to be a feasible method of detecting unknown ships. In this study, acoustic modeling code ¡§Acoustic Module for Sea-surface Noise¡¨ (AMSN) is applied by using the ship position information from AIS, to calculate the related underwater noise sound field of Kaohsiung Harbor. Discussions were made on the dependence of noise level variation with ship density. As a conclusion, with sufficient understanding of sound field statistics of harbor, any anomaly of noise level can be an indication of hostile intrusion, thus harbor security can be further assured.

Main goal of graduation theses is analysis possibilities to use logistic technologies in food company Fontea. This analysis is focused on material and informative flows in her logistic chain including analysis of key factors. These factors are important for implementation of chosen technologies. Subsequently were analyzed customer department, sales department, production department, purchasing department and shipping department. There were found several principal problems. Customer department staffs have a problem with finding actual quantity of manufactured finished goods, because up-dating of computer system is provided only once a day. Most of orders are inserted manually in computer system. Staffs are not able to find out charges of manufactured finished goods. Similar problems were discovered also in the next departments. To fix the problem were suggested implementation of Quick Response and automatic identification of barcodes and Radio Frequency Identification (RFID).

Number of lanes is a basic roadway attribute that is widely used in many transportation applications. Traditionally, number of lanes is collected and updated through field surveys, which is expensive especially for large coverage areas with a high volume of road segments. One alternative is through manual data extraction from high-resolution aerial images. However, this is feasible only for smaller areas. For large areas that may involve tens of thousands of aerial images and millions of road segments, an automatic extraction is a more feasible approach. This dissertation aims to improve the existing process of extracting number of lanes from aerial images automatically by making improvements in three specific areas: (1) performance of lane model, (2) automatic acquisition of external knowledge, and (3) automatic lane location identification and reliability estimation. In this dissertation, a framework was developed to automatically recognize and extract number of lanes from geo-rectified aerial images. In order to address the external knowledge acquisition problem in this framework, a mapping technique was developed to automatically estimate the approximate pixel locations of road segments and the travel direction of the target roads in aerial images. A lane model was developed based on the typical appearance features of travel lanes in color aerial images. It provides more resistance to “noise” such as presence of vehicle occlusions and sidewalks. Multi-class classification test results based on the K-nearest neighbor, logistic regression, and Support Vector Machine (SVM) classification algorithms showed that the new model provides a high level of prediction accuracy. Two optimization algorithms based on fixed and flexible lane widths, respectively, were then developed to extract number of lanes from the lane model output. The flexible lane-width approach was recommended because it solved the problems of error-tolerant pixel mapping and reliability estimation. The approach was tested using a lane model with two SVM classifiers, i.e., the Polynomial kernel and the Radial Basis Function (RBF) kernel. The results showed that the framework yielded good performance in a general test scenario with mixed types of road segments and another test scenario with heavy plant occlusions.

This diploma thesis covers the procedure of process optimalization within the project of design and implementation of automatic identification system in DTZ Liberec s.r.o. The theoretical part defines the general scope of business processes, their management and improvement. Furthermore, it gives an account of the fundaments of project management and presents the basic technologies of automatic identification, as well as possibilities for its use. The practical part describes the project and presents its outcomes. After introduction into company's situation, basic issues connected with project team assembly are outlined. The core of the practical part covers the identification of processes in areas, where automatic identification is being implemented, definition of specifics of their use and design of process changes with the goal of increasing their efficiency. In conclusion, the values of process measurement indicators, gained within the test run of the implemente system, are presented. The goal of the thesis is to propose process optimalization in chosen company using automatic identification technology.

In the long term it is beneficial to a country's economy to exploit the maritime environment surrounding it responsibly. It is also beneficial to protect this environment from poaching and pollution. To achieve this the responsible parties of a country must have an awareness of what is transpiring in the maritime domain. Synthetic aperture radar can provide an image, regardless of weather or light conditions, of the ocean showing most vessels therein. To monitor the ocean, using synthetic aperture radar imagery, at the lowest cost would require large swath synthetic aperture radar imagery. There exists a trade-off between large swath imagery and the image's resolution resulting in the largest swath image having the poorest resolution. Existing research has shown that it is possible to use coarse resolution synthetic aperture radar imagery to detect vessels at sea, but little work has been done on classifying those vessels. This research aims to investigate the coarse resolution classification information gap. This is done by using a dataset of matching synthetic aperture radar and ship transponder data to train a statistical classification algorithm in order to classify or estimate the length of vessels based on features extracted from their synthetic aperture radar image. The results of this research show that coarse resolution (approximately 40 m per pixel) synthetic aperture radar imagery is able to estimate vessel size for larger classes and provides insight on which vessel classes would require finer resolutions in order to be detected and classified reliably. The range of smaller vessel classes is usually limited to ports and fishing zones. These zones can be mapped using historical vessel transponder data and so a dedicated surveillance campaign can be optimised to use higher resolution products in these areas. The size estimation from the machine learning algorithm performs better than current techniques. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted