Digital techniques

The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user's high-level interpretation of an image and the information that can be extracted from an image's physical properties. Content based image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. High-level descriptions of images are more capable of capturing the semantic meaning of image content, but it is not always practical to collect this information. Thus, both content-based and human-generated information is considered in this work. A content-based method of retrieving images using a computational model of visual attention was proposed, implemented, and evaluated. This work is based on a study of contemporary research in the field of vision science, particularly computational models of bottom-up visual attention. The use of computational models of visual attention to detect salient by design regions of interest in images is investigated. The method is then refined to detect objects of interest in broad image databases that are not necessarily salient by design. An interface for image retrieval, organization, and annotation that is compatible with the attention-based retrieval method has also been implemented. It incorporates the ability to simultaneously execute querying by image content, keyword, and collaborative filtering. The user is central to the design and evaluation of the system. A game was developed to evaluate the entire system, which includes the user, the user interface, and retrieval methods.

Cache memory is used, in most single-core and multi-core processors, to improve performance by bridging the speed gap between the main memory and CPU. Even though cache increases performance, it poses some serious challenges for embedded systems running real-time applications. Cache introduces execution time unpredictability due to its adaptive and dynamic nature and cache consumes vast amount of power to be operated. Energy requirement and execution time predictability are crucial for the success of real-time embedded systems. Various cache optimization schemes have been proposed to address the performance, power consumption, and predictability issues. However, currently available solutions are not adequate for real-time embedded systems as they do not address the performance, power consumption, and execution time predictability issues at the same time. Moreover, existing solutions are not suitable for dealing with multi-core architecture issues. In this dissertation, we develop a methodology through cache optimization for real-time embedded systems that can be used to analyze and improve execution time predictability and performance/power ratio at the same time. This methodology is effective for both single-core and multi-core systems. First, we develop a cache modeling and optimization technique for single-core systems to improve performance. Then, we develop a cache modeling and optimization technique for multi-core systems to improve performance/power ratio. We develop a cache locking scheme to improve execution time predictability for real-time systems. We introduce Miss Table (MT) based cache locking scheme with victim cache (VC) to improve predictability and performance/power ratio. MT holds information about memory blocks, which may cause more misses if not locked, to improve cache locking performance.

Perceptual video coding has been a promising area during the last years. Increases in compression ratios have been reported by applying foveated video coding techniques where the region of interest (ROI) is selected by using a computational attention model. However, most of the approaches for perceptual video coding only use visual features ignoring the auditory component. In recent physiological studies, it has been demonstrated that auditory stimuli affects our visual perception. In this work, we validate some of those physiological tests using complex video sequence. We designed and developed a web-based tool for video quality measurement. After conducting different experiments, we observed that in the general reaction time to detect video artifacts was higher when video was presented with the audio information. We observed that emotional information in audio guide human attention to particular ROI. We also observed that sound frequency change spatial frequency perception in still images.

Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods.

Implementing Shamir's secret sharing scheme using floating point arithmetic would provide a faster and more efficient secret sharing scheme due to the speed in which GPUs perform floating point arithmetic. However, with the loss of a finite field, properties of a perfect secret sharing scheme are not immediately attainable. The goal is to analyze the plausibility of Shamir's secret sharing scheme using floating point arithmetic achieving the properties of a perfect secret sharing scheme and propose improvements to attain these properties. Experiments indicate that property 2 of a perfect secret sharing scheme, "Any k-1 or fewer participants obtain no information regarding the shared secret", is compromised when Shamir's secret sharing scheme is implemented with floating point arithmetic. These experimental results also provide information regarding possible solutions and adjustments. One of which being, selecting randomly generated points from a smaller interval in one of the proposed schemes of this thesis. Further experimental results indicate improvement using the scheme outlined. Possible attacks are run to test the desirable properties of the different schemes and reinforce the improvements observed in prior experiments.

The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies.

Wireless devices in wireless networks are powered typically by small batteries that are not replaceable nor recharged in a convenient way. To prolong the operating lifetime of networks, energy efficiency is indicated as a critical issue and energy-efficient resource allocation designs have been extensively developed. We investigated energy-efficient schemes that prolong network operating lifetime in wireless sensor networks and in wireless relay networks. In Chapter 2, the energy-efficient resource allocation that minimizes a general cost function of average user powers for small- or medium-scale wireless sensor networks, where the simple time-division multiple-access (TDMA) is adopted as the multiple access scheme. A class of Ç-fair cost-functions is derived to balance the tradeoff between efficiency and fairness in energy-efficient designs. Based on such cost functions, optimal channel-adaptive resource allocation schemes are developed for both single-hop and multi-hop TDMA sensor networks. In Chapter 3, optimal power control methods to balance the tradeoff between energy efficiency and fairness for wireless cooperative networks are developed. It is important to maximize power efficiency by minimizing power consumption for a given quality of service, such as the data rate; it is also equally important to evenly or fairly distribute power consumption to all nodes to maximize the network life. The optimal power control policy proposed is derived in a quasi-closed form by solving a convex optimization problem with a properly chosen cost-function. To further optimize a wireless relay network performance, an orthogonal frequency division multiplexing (OFDM) based multi-user wireless relay network is considered in Chapter 4.

This thesis proposes to estimate the speed of a moving acoustic source by either linear or non linear processing of the resulting Doppler shift present in a high-frequency pilot tone. The source is an acoustic modem (Hermes) which currently uses moving average to estimate and compensate for Doppler shift. A new auto regressive approach to Doppler estimation (labeled IIR method in the text) promises to give a better estimate. The results for a simulated peak velocity of 2 m/s in the presence of additive noise showed an RMSE of 0.23 m/s using moving average vs. 0.00018 m/s for the auto regressive approach. The SNR was 75 dB. The next objective was to compare the estimated Doppler velocity obtained using the two algorithms with the experimental values recorded in real time. The setup consisted of a receiver hydrophone attached to a towing carriage that moved with a known velocity with respect to a stationary acoustic source. The source transmitted 375 kHz pilot tone. The received pilot tone data were preprocessed using the two algorithms to estimate both Doppler shift and Doppler velocity. The accuracy of the algorithms was compared against the true velocity values of the carriage. The RMSE for a message from experiments conducted indoor for constant velocity of 0.4 m/s was 0.6055 m/s using moving average, 0.0780 m/s using auto regressive approach. The SNIR was 6.3 dB.

Flow over a rough surface is known to radiate sound as a dipole source that is directional. In order to better understand this source, measurements are being made in a wind tunnel using a microphone array. The measurements collected by a microphone array are beamformed to give a source image and can be deconvolved with an assumed point spread function in order to obtain the source levels. This thesis considers alternative analysis algorithms that can be used to analyze wind tunnel data. Only numerical examples of how these algorithms work will be presented and the analysis of real data will be considered in later studies. It will be shown how estimates can be made of the source directivity by comparing the measured data with a theoretical source model and minimizing the error between the model and the measurements.

Blind source separation (BSS) refers to a class of methods by which multiple sensor signals are combined with the aim of estimating the original source signals. Independent component analysis (ICA) is one such method that effectively resolves static linear combinations of independent non-Gaussian distributions. We propose a method that can track variations in the mixing system by seeking a compromise between adaptive and block methods by using mini-batches. The resulting permutation indeterminacy is resolved based on the correlation continuity principle. Methods employing higher order cumulants in the separation criterion are susceptible to outliers in the finite sample case. We propose a robust method based on low-order non-integer moments by exploiting the Laplacian model of speech signals. We study separation methods for even (over)-determined linear convolutive mixtures in the frequency domain based on joint diagonalization of matrices employing time-varying second order statistics. We investigate the sources affecting the sensitivity of the solution under the finite sample case such as the set size, overlap amount and cross-spectrum estimation methods.