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Sound source localization with microphone arrays has received considerable attention as a means for the automated tracking of individuals in an enclosed space and as a necessary component of any general-purpose speech capture and automated camera pointing system. A novel computationally efficient method compared to traditional source localization techniques is proposed and is both theoretically and experimentally investigated in this research. This thesis first reviews the previous work in this area. The evolution of a new localization algorithm accompanied by an array structure for audio signal localization in three dimensional space is then presented. This method, which has similarities to the structure of the eye, consists of a novel hemispherical microphone array with microphones on the shell and one microphone in the center of the sphere. The hemispherical array provides such benefits as 3D coverage, simple signal processing and low computational complexity. The signal processing scheme utilizes parallel computation of a special and novel closeness function for each microphone direction on the shell. The closeness functions have output values that are linearly proportional to the spatial angular difference between the sound source direction and each of the shell microphone directions. Finally by choosing directions corresponding to the highest closeness function values and implementing linear weighted spatial averaging in those directions we estimate the sound source direction. The experimental tests validate the method with less than 3.10 of error in a small office room. Contrary to traditional algorithmic sound source localization techniques, the proposed method is based on parallel mathematical calculations in the time domain. Consequently, it can be easily implemented on a custom designed integrated circuit.

Labels are used for managing warehouse environments by collecting information from existing items on shelves and racks. Labels enable description and identification of items accurately in a short time. Although lot of research have been done in the field of barcode detection, the present methods for detection are applicable at a short distance from the camera and with a clear background. Therefore, label detection from captured images is challenging especially with a large and complex background. Once a label is detected, it is ready for next process of recognition, to read out the stored information in texts and barcodes. In this thesis, we compared methods from previous works and implemented the most suitable one for detecting one-dimensional (1D) barcodes available on the captured images by standard lens. We created a dataset for label detection with an assumption on background color and we continued processing by K-means clustering and classification. After localizing label regions, a projection for determining a different candidate area is done. We have worked on two types of barcodes, one-dimensional (1D) and Data Matrix as a two-dimensional (2D) barcode. The results show a good performance of the system in terms of images, which are the most important issue in terms of industrial detection.

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Localization is very important for self-organizing wireless networks. The localization process involves two main steps: ranging, i.e., estimating the distance between an unlocalized node and the anchor nodes within its range, and the localization algorithm to compute the location of the unlocalized nodes using the anchor coordinates and the estimated ranges. To be able to estimate the distance, the receiver needs to detect the arrival time of the received signals precisely. Thus, the first part of this research is related to time synchronization. We propose two new symbol timing offset estimation (STO) algorithms that can detect the start of an orthogonal frequency division multiplexing (OFDM) symbol more accurately than others in a Rayleigh fading channel. OFDM is used to perform timing synchronization because it is incorporated inmany current and future wireless systems such as 802.11, WiMAX, wireless USB, and WiMedia. The first proposed algorithm uses a metric that is calculated recursively. Two estimation methods are considered: one using the average of the metric results, and the other using the median. The second approach uses a preamble designed to have a maximum timing metric for the correct location and very small values otherwise. These algorithms are shown to outperform recent algorithms in the literature. In the second part of this dissertation we explore the second step of the localization problem. There are two kinds of localization: range-free and range-based. A new distributed range-free localization algorithm is proposed where every unlocalized node forms two sets of anchors. The first set contains one-hop anchors from the unlocalized node. The second set contains two-hop and three-hop anchors away from the unlocalized node. Each unlocalized node uses the intersections between the ranging radii of these anchors to estimate its position. Four different range-based localization algorithms are proposed. These algorithms use techniques from data mining to process the intersection points between an unlocalized node and nearby anchors. The first proposed scheme is based on decision tree classification to select a group of intersection points. The second is based on the decision tree classification and K-means clustering algorithms applied to the selected intersection points by the decision trees. The third is based on decision tree classification and the density-based spatial clustering of applications with noise (DBSCAN) algorithm applied to the intersection points selected by decision trees. The last approach uses the density-based outlier detection (DBOD) algorithm. DBOD assigns density values to each point being used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. These proposed approaches are shown to outperform recent algorithms in the literature. / Graduate

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