Aliasing Reduction in Staring Infrared Imagers Utilizing Subpixel Techniques

We introduce and analyze techniques for the reduction of aliased signal energy in a staring infrared imaging system. A standard staring system uses a fixed two-dimensional detector array that corresponds to a fixed spatial sampling frequency determined by the detector pitch or spacing. Aliasing will occur when sampling a scene containing spatial frequencies exceeding half the sampling frequency. This aliasing can significantly degrade the image quality. The aliasing reduction schemes presented here, referred to as microscanning, exploit subpixel shifts between time frames of an image sequence. These multiple images are used to reconstruct a single frame with reduced aliasing. If the shifts are controlled, using a mirror or beam steerer for example, one can obtain a uniformly sampled microscanned image. The reconstruction in this case can be accomplished by a straightforward interlacing of the time frames. If the shifts are uncontrolled, the effective sampling may be nonuniform and reconstruction becomes more complex. A sampling model is developed and the aliased signal energy is analyzed for the microscanning techniques. Finally, a number of experimental results are presented that illustrate the perlormance of the microscanning methods

Multispectral processing based on groups of resolution elements

Several nine-point rules are defined and compared with previously studied rules. One of the rules performed well in boundary areas, but with reduced efficiency in field interiors; another combined best performance on field interiors with good sensitivity to boundary detail. The basic threshold gradient and some modifications were investigated as a means of boundary point detection. The hypothesis testing methods of closed-boundary formation were also tested and evaluated. An analysis of the boundary detection problem was initiated, employing statistical signal detection and parameter estimation techniques to analyze various formulations of the problem. These formulations permit the atmospheric and sensor system effects on the data to be thoroughly analyzed. Various boundary features and necessary assumptions can also be investigated in this manner

Methodische Ansätze zur Analyse biomechanischer Zeitreihendaten

Die vorliegende Dissertation beschäftigt sich mit der Frage inwiefern der Einsatz von Zeitreihenmethoden - also Methoden, die den kontinuierlichen Charakter biomechanischer Zeitreihendaten berücksichtigen - einen aus methodischer Sicht günstigeren Auswertungsansatz darstellt als herkömmliche, diskrete Methoden. Damit liefert die Arbeit einen wichtigen Beitrag zur Überwindung der postulierten Konfidenzkrise biomechanisch-bewegungswissenschaftlicher Forschung, nach der sich die Ergebnisse vieler Studien aufgrund u. a. methodischer Defizite nicht oder nur eingeschränkt reproduzieren lassen. Dabei konnte in drei Teilstudien für verschiedene methodische Bereiche (Inferenzstatistik, Klassifikation, Trenderkennung) gezeigt werden, dass der Einsatz von Zeitreihenmethoden bekannte Probleme diskreter Methoden erfolgreich addressieren kann. Die vorgestellte Arbeit regt über das spezifische Thema hinaus zu einem kritischeren Umgang mit methodischen Aspekten an und zeigt mögliche Lösungsansätze auf

Towards CO2 emission monitoring with passive air- and space-borne sensors

Coal-fueled power plants are responsible for 30 % of anthropogenic carbon dioxide (CO2) emissions and can therefore be considered important drivers of climate warming. The 2015 Paris Climate Accord has established a global stock take mechanism, which will assess the progress of global carbon emission reduction policies in five-yearly tallies of worldwide emissions. However, there exists no independent monitoring network, which could verify such stock takes. Remote sensing of atmospheric CO2 concentrations from air- and space-borne sensors could provide the means of monitoring localized carbon sources, if their ground sampling distance is sufficiently fine (i.e. below the kilometer scale). Increased spatial resolution can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra. The present thesis aims to add to the methodology of remote CO2 monitoring approaches by studying the compromise between spectral and spatial resolution with CO2 retrievals from three different sensors. First, the trade-off between coarse spectral resolution and retrieval performance is discussed for a hypothetical imaging spectrometer which could reach a spatial resolution of ~50×50 m2 by measuring backscattered sunlight in the short wave infrared spectral range at a resolution of ∆λ ~ 1 nm. To this end, measurements of the Greenhouse gases Observing SATellite (GOSAT) at ∆λ = 0.1 nm are artificially degraded to coarser spectral resolutions to emulate the proposed sensor. CO2 column retrievals are carried out with the native and degraded spectra and the results are compared with each other, while data from the ground based Total Carbon Column Observing Network (TCCON) serve as independent reference data. This study identifies suitable retrieval windows in the short wave infrared spectral range and a favorable spectral resolution for a CO2 monitoring mission. Second, CO2 column retrievals are carried out with measurements of the air-borne AVIRIS-NG sensor at a spectral resolution of ∆λ = 5 nm. This case study identifies advantageous CO2 retrieval configurations, which minimize correlations between retrieval parameters, near two coal-fired power plants. A bias correction method is proposed for the retrievals and a plume mask is applied to the retrieved CO2 enhancements to separate the CO2 emission signal from the atmospheric background. Emission rates of the two facilities are calculated under consideration of the local wind speed, compared to a public inventory and discussed in terms of their uncertainties. Third, CO2 retrievals are extended to spectral resolutions on the order of ∆λ ~ 10 nm by analyzing spectra of the specMACS imager near a small power plant. Retrieval effects that hamper the detection of the source signal are discussed

Geometric Transformation Techniques for Digital Images: A Survey

This survey presents a wide collection of algorithms for the geometric transformation of digital images. Efficient image transformation algorithms are critically important to the remote sensing, medical imaging, computer vision, and computer graphics communities. We review the growth of this field and compare all the described algorithms. Since this subject is interdisciplinary, emphasis is placed on the unification of the terminology, motivation, and contributions of each technique to yield a single coherent framework. This paper attempts to serve a dual role as a survey and a tutorial. It is comprehensive in scope and detailed in style. The primary focus centers on the three components that comprise all geometric transformations: spatial transformations, resampling, and antialiasing. In addition, considerable attention is directed to the dramatic progress made in the development of separable algorithms. The text is supplemented with numerous examples and an extensive bibliography

The quantitative analysis of transonic flows by holographic interferometry

This thesis explores the feasibility of routine transonic flow analysis by holographic interferometry. Holography is potentially an important quantitative flow diagnostic, because whole-field data is acquired non-intrusively without the use of particle seeding. Holographic recording geometries are assessed and an image plane specular illumination configuration is shown to reduce speckle noise and maximise the depth-of-field of the reconstructed images. Initially, a NACA 0012 aerofoil is wind tunnel tested to investigate the analysis of two-dimensional flows. A method is developed for extracting whole-field density data from the reconstructed interferograms. Fringe analysis errors axe quantified using a combination of experimental and computer generated imagery. The results are compared quantitatively with a laminar boundary layer Navier-Stokes computational fluid dynamics (CFD) prediction. Agreement of the data is excellent, except in the separated wake where the experimental boundary layer has undergone turbulent transition. A second wind tunnel test, on a cone-cylinder model, demonstrates the feasibility of recording multi-directional interferometric projections using holographic optical elements (HOE’s). The prototype system is highly compact and combines the versatility of diffractive elements with the efficiency of refractive components. The processed interferograms are compared to an integrated Euler CFD prediction and it is shown that the experimental shock cone is elliptical due to flow confinement. Tomographic reconstruction algorithms are reviewed for analysing density projections of a three-dimensional flow. Algebraic reconstruction methods are studied in greater detail, because they produce accurate results when the data is ill-posed. The performance of these algorithms is assessed using CFD input data and it is shown that a reconstruction accuracy of approximately 1% may be obtained when sixteen projections are recorded over a viewing angle of ±58°. The effect of noise on the data is also quantified and methods are suggested for visualising and reconstructing obstructed flow regions

Indoor Source Localization of Radio Frequency Transmitters Using Blind Channel Identification Techniques

Locating transmitters is a research area that is becoming increasingly relevant as technology advances. It is especially useful for determining the location of livestock, drones, keys, phones, tablets, etc. As a result of this push for locating devices, many algorithms have been developed to determine source locations. Most source location algorithms and techniques rely on a line of sight , or a direct path between the source and the receivers to provide accurate results. Indoor environments pose a challenge to locating transmitters due to the many surfaces that allow radio waves to interact (reflect, refract, and generally distort) with them. Because of the effects of the radio wave interactions, a direct path from the transmitter to the receivers may not be possible inside, increasing the difficulty. This problem is further augmented when the transmitter is transmitting an unknown signal in an unknown environment. This research derives algorithms to address these issues. The algorithms are tested via simulations and real-world environmental testing

Millimeter wave link configuration with hybrid MIMO architectures

The use of multiple antennas, widely known as MIMO technology, is a key feature to deploy mmWave communication systems enabling high-data-rate applications. With more than two decades of global experience in deploying Wi-Fi and cellular communication using sub-6 GHz frequency bands, simply repurposing these designs for mmWave bands would fail to account for additional propagation impairments and circuit design constraints at these higher frequencies. A solution to overcome the propagation challenges is the use of multiple directional communication beams, whereby proper alignment between transceivers provides sufficient link quality to enable reliable decoding of the transmitted data. In this dissertation, efficient link configuration solutions suitable for mmWave cellular communications are developed. To gain some insight into the achievable performance of mmWave systems, two broadband channel-estimation-based link configuration solutions are proposed for MIMO-OFDM systems, in which both the transmitter and receiver are assumed to be perfectly synchronized. The proposed solution exploits the spatially common sparsity in the mmWave channel and enables efficient acquisition of the CSI while allowing the use of multiple RF chains on both the transmitter and receiver sides. In a simplified scenario, the CRLB for the channel estimation problem is derived, and the proposed channel estimation algorithms are shown to both outperform prior work in communication performance and exhibit excellent estimation performance. Furthermore, the proposed algorithms are assessed in a more challenging scenario with realistic channel parameters, and it is shown that both near-optimal spectral efficiency and low BER can be attained with lower overhead and computational complexity than prior solutions. Next, the impact of imperfect CFO synchronization on the channel estimation problem is analyzed under a narrowband channel model. The CRLB for the estimation of the different unknown parameters involved in the problem is theoretically analyzed, and closed-form expressions are provided for the estimation of the different parameters. Under a joint estimation-theoretic and CS framework, a low-complexity multi-stage solution is proposed to estimate both the different unknown synchronization parameters and the large-dimensional mmWave MIMO channel. Different trade-offs between estimation, spectral efficiency, and overhead performance are exposed, and the proposed estimators are shown to be asymptotically optimal in the low SNR regime. The proposed solution is assessed under a channel model with several clusters and rays per cluster, and is shown to attain near-optimal spectral efficiency values in both the low and high SNR regimes. The computational complexity of the proposed solution is also analyzed, in which it is shown to achieve a marginal increase in computational complexity with respect to the solution proposed in the previous contribution. Finally, the impact of TO, CFO, and PN impairments on the channel estimation problem is analyzed under a broadband channel model. The problem of time-frequency synchronization under PN impairments is theoretically analyzed, and the proposed solutions to the synchronization problem are exploited to estimate the frequency-selective mmWave MIMO channel. The hybrid CRLB for the estimation of the different synchronization impairments is analyzed, and closed-form expressions leveraging the information coupling between the different impairments are provided. The previously proposed joint estimation-theoretic and CS framework is extended to frequency-selective scenarios, and two low-complexity multi-stage solutions are proposed to estimate both the different synchronization impairments and the large-dimensional mmWave MIMO channel. The first solution relies on a batch-processing LMMSE-based EM algorithm to estimate the different synchronization impairments, while the second solution uses a sequential-processing EKF-RTS-based EM algorithm, thereby reducing computational complexity. Thereafter, both the hybrid CRLB for the estimation of the equivalent beamformed complex channels and the estimates for these parameters are exploited to estimate the large-dimensional frequency-selective mmWave MIMO channel. Finally, a joint PN and data detection algorithm is proposed for data transmission under the 5G NR frame structure. The proposed solutions are evaluated using a 5G NR-based channel model, and different trade-offs between estimation performance, computational complexity, overhead, achievable spectral efficiency and BER are exposed, and comparisons with prior work are also provided. The results show that mmWave link configuration using hybrid MIMO architectures can be established with low overhead without assuming synchronization, even in the low SNR regime.Electrical and Computer Engineerin

Towards CO2 Emission Monitoring with Passive Air- and Space-Borne Sensors

Coal-fueled power plants are responsible for 30% of anthropogenic carbon dioxide (CO2) emissions and can therefore be considered important drivers of climate warming. The 2015 Paris Climate Accord has established a global stock take mechanism, which will assess the progress of global carbon emission reduction policies in five-yearly tallies of worldwide emissions. However, there exists no independent monitoring network, which could verify such stock takes. Remote sensing of atmospheric CO2 concentrations from air- and space-borne sensors could provide the means of monitoring localized carbon sources, if their ground sampling distance is sufficiently fine (i.e. below the kilometer scale). Increased spatial resolution can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra. The present thesis aims to add to the methodology of remote CO2 monitoring approaches by studying the compromise between spectral and spatial resolution with CO2 retrievals from three different sensors. First, the trade-off between coarse spectral resolution and retrieval performance is discussed for a hypothetical imaging spectrometer which could reach a spatial resolution of ∼ 50 × 50 m² by measuring backscattered sunlight in the short wave infrared spectral range at a resolution of ∆λ ∼ 1 nm. To this end, measurements of the Greenhouse gases Observing SATellite (GOSAT) at ∆λ = 0.1 nm are artificially degraded to coarser spectral resolutions to emulate the proposed sensor. CO2 column retrievals are carried out with the native and degraded spectra and the results are compared with each other, while data from the ground based Total Carbon Column Observing Network (TCCON) serve as independent reference data. This study identifies suitable retrieval windows in the short wave infrared spectral range and a favorable spectral resolution for a CO2 monitoring mission. Second, CO2 column retrievals are carried out with measurements of the airborne AVIRIS-NG sensor at a spectral resolution of ∆λ = 5 nm. This case study identifies advantageous CO2 retrieval configurations, which minimize correlations between retrieval parameters, near two coal-fired power plants. A bias correction method is proposed for the retrievals and a plume mask is applied to the retrieved CO2 enhancements to separate the CO2 emission signal from the atmospheric background. Emission rates of the two facilities are calculated under consideration of the local wind speed, compared to a public inventory and discussed in terms of their uncertainties. Third, CO2 retrievals are extended to spectral resolutions on the order of ∆λ ∼ 10 nm by analyzing spectra of the specMACS imager near a small power plant. Retrieval effects that hamper the detection of the source signal are discussed