Interference Suppression for Spread Spectrum Signals Using Adaptive Beamforming and Adaptive Temporal Filter

Interference and jamming signals are a serious concern in an operational military communication environment. This thesis examines the utility and performance of combining adaptive temporal filtering with adaptive spatial filtering (i.e. adaptive beamforming) to improve the signal-to-jammer ratio (SJR) in the presence of narrowband and wideband interference. Adaptive temporal filters are used for narrowband interference suppression while adaptive beamforming is used to suppress wideband interference signals. A procedure is presented for the design and implementation of a linear constraints minimum variance generalized sidelobe canceler (LCMV-GSC) beamformer. The adaptive beamformer processes the desired signal with unity gain while simultaneously and adaptively minimizing the output due to any undesired signal. Using the LCMV-GSC beamformer with a least mean squares (LMS) adaptive algorithm, it was shown that the tapped delay line (TDL) adaptive antenna array is more effective for the suppression of wideband jammer suppression than the linear array sensors (LAS) adaptive antenna array. Also a new technique for adaptive beamforming is presented which improves wideband interference suppression in a frequency-hopped environment. The output SJR improvement for the new technique compared to the conventional technique is as much as 15dB. Sometimes, multipath signals and jammers generated by a smart enemy are correlated with the desired signal which destroys the traditional beamformer\u27s performance. After performing a spatial smoothing technique, adaptive beamforming can also be effective in suppressing the jamming signals that are highly correlated with the desired signal

An Experimental Study of the Effects of Representational Data Quality on Decision Performance

The effects of information quality and the importance of information have been reported in the Information Systems literature. However, little has been learned about the impact of data quality (DQ) on decision performance. Representational DQ means that data must be interpretable, easy to understand, and represented concisely and consistently. This study explores the effects of representational DQ and task complexity on decision performance by conducting a laboratory experiment. Based on two levels of representational DQ and two levels of task complexity, this study had a 2 x 2 factorial design. The dependent variables were problem-solving accuracy and time. The results demonstrated that the effects of representational DQ on decision performance were significant. The findings suggest that decision makers can expect to improve their decision performance by enhancing representational DQ. This research extends a body of research examining the effects of factors that can be tied to human decision-making performance

Shear behavior of a shear thickening fluid-impregnated aramid fabrics at high shear rate

Shear-thickening fluid-impregnated aramid (STF-im-AR) fabrics have been manufactured for advanced soft body armor applications for which they provide improved ballistic and stab resistances. It is not yet clear whether or not such improvements can be attributed solely to the STF. In this study, the rate-dependent behavior of an STF-im-AR fabric was investigated at the fabric level, using uniaxial tensile, bias-extension, and picture-frame tests. Rate-dependent behavior of the STF-im-AR fabric was observed during uniaxial tensile testing; however, the effect of the STF treatment was slight and consistent with only the inherent effect of the polymeric nature of its constituent fibers. The shear rigidity of the STF-im-AR fabric increased, due to the presence of the STF and the sensitivity of the fabric's shear stiffness to changes in the shear strain rate also increased slightly. This rate-sensitive shear stiffness of STF-im-AR fabrics may contribute to improved ballistic and stab resistances

Coherence of a field-gradient-driven singlet-triplet qubit coupled to many-electron spin states in 28Si/SiGe

Engineered spin-electric coupling enables spin qubits in semiconductor nanostructures to be manipulated efficiently and addressed individually. While synthetic spin-orbit coupling using a micromagnet is widely used for driving qubits based on single spins in silicon, corresponding demonstration for encoded spin qubits is so far limited to natural silicon. Here, we demonstrate fast singlet-triplet qubit oscillation (~100 MHz) in a gate-defined double quantum dot in $^{28}$Si/SiGe with an on-chip micromagnet with which we show the oscillation quality factor of an encoded spin qubit exceeding 580. The coherence time $\textit{T}_{2}$* is analyzed as a function of potential detuning and an external magnetic field. In weak magnetic fields, the coherence is limited by fast noise compared to the data acquisition time, which limits $\textit{T}_{2}$* < 1 ${\mu}$s in the ergodic limit. We present evidence of sizable and coherent coupling of the qubit with the spin states of a nearby quantum dot, demonstrating that appropriate spin-electric coupling may enable a charge-based two-qubit gate in a (1,1) charge configuration

Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)-which has high temporal, spatial, and spectral resolutions-is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7-589.6, 603.6-631.8, 641.2-655.35, 664.8-679.0, 698.0-712.3, and 731.4-784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir

Towards multiplexed immunofluorescence of 3D tissues

Abstract Profiling molecular expression in situ allows the integration of biomolecular and cellular features, enabling an in-depth understanding of biological systems. Multiplexed immunofluorescence methods can visualize tens to hundreds of proteins from individual tissue samples, but their application is usually limited to thin tissue sections. Multiplexed immunofluorescence of thick tissues or intact organs will enable high-throughput profiling of cellular protein expression within 3D tissue architectures (e.g., blood vessels, neural projections, tumors), opening a new dimension in diverse biological research and medical applications. We will review current multiplexed immunofluorescence methods and discuss possible approaches and challenges to achieve 3D multiplexed immunofluorescence