Browsing by Subject "Detection"
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Item Open Access Automatic detection of solar photovoltaic arrays in high resolution aerial imagery(Applied Energy, 2016-12) Malof, JM; Bradbury, K; Collins, LM; Newell, RG© 2016 Elsevier Ltd The quantity of small scale solar photovoltaic (PV) arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV array location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135 km 2 ) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV array locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV array detection, but with significant potential for improvement in estimating the precise shape/size of the PV arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.Item Open Access Detection and Classification of Whale Acoustic Signals(2016) Xian, YinThis dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.
In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.
In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.
Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.
We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.
Item Open Access Detection and Quantification of Single-walled Carbon Nanotubes in Environmental and Biological Samples for Evaluation of Fate, Transport and Bioaccumulation(2017) Liu, XuehongSingle-walled carbon nanotubes (SWCNT) are unique, anthropogenic allotropes of nanoparticulate black carbon. As numerous industrial and commercial uses of SWCNT result the heavy expansion of production of this material, the release of SWCNT is likely to occur, increasing their level in air, water and soil. SWCNTs have been shown to cause adverse impact in organisms from direct exposure through ingestion or inhalation. In addition to direct exposure, SWCNT can also induce toxicity to organisms by indirect exposure such as adsorption of hydrophobic contaminants (HOCs). One unique property of SWCNT is the quantized nature of their electronic structure, which is dependent on the chiral wrapping angle of the sp2 hybridized graphene sheet that comprises the wall of each SWNT species. Using probe HOCs – one planar polycyclic aromatic hydrocarbon (PAH)14 C-naphthalene and one halogenated aromatic 14 C-hexachlorobenzene and purified conductive and semiconductive SWCNT species, my first study aimed at assessing the role of SWCNT electronic structure on HOC sorption. Despite their differences in electronic structures, the results indicated that overall the electronic structure does not influence the adsorption of HOCs. However, due to the large specific surface area, SWCNT have a general high affinity for HOCs. Upon release of SWCNT into aquatic environment, SWCNT have the potential to affect the distribution of organic contaminants by acting as strong sorbent.
A significant barrier to studying toxicity of SWCNT to animal models is the lack of in vivo techniques to track and quantify SWCNT for assessing their distribution, transport and bioaccumulation. The fluorescence resulting from the unique band gap of each species of semiconductive SWCNT allows the detection and quantification of a bulky SWCNT sample using near infrared fluorescence spectroscopy (NIRF). NIRF is highly sensitive to detect SWCNT in biological tissues due to the low fluorescence in the near infrared region from biological samples. Two exposure routes were investigated using NIRF: ingestion from dietary track using fathead minnow (FHM) fish model in an aquatic environment and inhalation through lung using mouse model. The SWCNT extraction conditions were optimized and validated using spike recovery experiments. SWCNT were extracted from fish tissues, intestine, and liver using ultrasonic extraction in 2% sodium deoxycholate1extraction. Proteinase K digestion was needed for dissolving mouse lung prior to SDC extraction. The quantification results showed that while SWCNT readily passed through fish dietary track with minimal partition into the lumen tissue and caused no acute toxicity; SWCNT was less mobile in respiratory system and was responsible for the lung-term pulmonary disease induced.
The fate, transport and bioaccumulation of SWCNT are essential information for risk assessment and making environmental regulations for nanomaterials. Currently the lack of standardized sensitive characterization and quantitative analytical methods for SWCNT determination at the current levels in the environment is one major barrier for evaluation of their real impact to the environment. NIRF is sensitive for environmental samples. However, this technique is not sensitive to all types of SWCNT. Metal catalysts are widely used in synthetic production of SWCNTs, leading to total metal content ranging from 5 - 30%. The metal: metal ratios and metal: carbon ratios of SWCNT are very distinctive from many geological materials. A metal fingerprinting approach was developed by monitoring the metal type and metal: metal ratios, along with elemental carbon content. SWCNT can be principally quantified using inductive coupled plasma mass spectrometry (ICP-MS). Metal content, metal: metal ratios, elemental carbon and metal: carbon ratios were analyzed for two aerosol matrices, the urban dust NIST SRM 1649b and aerosol collected at Duke University using three types of SWCNT: SG65 SWCNT, SG65i SWCNT and P2 SWCNT. Results demonstrated that the metal finger approach worked well with all aerosol matrices with detection limits near ng m-3. It worked best with elements that were less abundant in the background such as Co and Y. This method offers a robust and economic approach for application to occupational spaces for monitoring possible SWCNT release.
Applying a similar approach in sediment presents a significant challenge as background metals present in sediment complicates such analyses. To overcome these challenges, we have applied density gradient ultracentrifuge (DGU) to isolate and separate SWCNT in sediment extracts prior to both NIRF and ICP-MS analysis. Several types of SWCNTs (arc discharge, CoMoCat, and HiPCO) were spiked and subsequently extracted from estuarine sediments. SWCNTs were separated into different bands after DGU, primarily into two distinct horizons (one showed near infrared fluorescence, while the other did not). Two techniques,near-infrared spectroscopy (NIRF) and ICP-MS, were applied for quantitation of SWCNTs in these bands. Results indicate excellent separation of SWCNT from interferences in sediments. We have also discovered an apparent disconnect between the metal catalyst particles and SWCNT during density gradient ultracentrifuge separation. It is clear that the SWCNT (within the NIRF band) is not physically associated with metal catalyst. This result was further confirmed using single-particle ICP-MS. Although DGU separation seems to be an outstanding method for isolating SWCNT from aquatic sediment for analysis, our current findings indicate that metal fingerprints derived from residual catalyst may not be a good tracer for SWCNT occurrence and fate in marine sediments, as the associated metal catalyst particles in SWCNT preparations might be transported in different ways relative to the SWCNT.
Overall, my research explored several analytical techniques to detect and quantify SWCNTs at their relevant concentration in various environmental matrices. These techniques will provide essential information for evaluating the environmental impact based on SWCNTs fate, transport and bioaccumulation in the environment.
Item Open Access Malignancy detection performance using excised breast tumor margin spectroscopic data and an optimal decision fusion based approach(2010) Oraby, SarahApproximately 20-70% of women with breast cancer who choose to undergo breast-conserving surgery (BCS) need to return to the operating table for re-excision [1]. Now devices utilizing optical spectroscopy are emerging as a new platform for intra-operative tumor margin assessment. This study aims to evaluate an optimal decision fusion approach for malignancy detection of measured spectroscopic data from a first-generation optical visible spectral imaging platform that can image the molecular composition of breast tumor margins by implementing a Monte Carlo method with measured diffuse reflectance [1, 2]. The device measures the diffuse reflectance across 450-600nm. After implementing the Monte Carlo algorithm the absorption and scattering spectra is derived and is used to provide insight on different optical properties present in the tissue mass [2]. Although the extracted optical properties may provide insight on the biological composition of a specimen, it may not be ideal for malignancy detection. Demographic factors may also affect a women's normal tissue breast composition, which makes malignancy detection more complicated. This optimal decision fusion approach implements the basic decision fusion methodology on acquired spectroscopic data to evaluate the effect on malignancy detection for different extracted optical parameters. The results of this automated and systematic approach indicate that a performance of 90% sensitivity and 68% specificity can be achieved with this approach for the diffuse reflectance spectrum, which outperforms the extracted optical properties. However, when only considering post-menopausal patients, the absorption spectrum can yield a sensitivity of 90% and specificity of 82% and has the best performance of all other features for this demographic group.
Item Open Access Non-recurrent Wideband Continuous Active Sonar(2014) Soli, Jonathan BoydThe Slow-time Costas or "SLO-CO" Continuous Active Sonar (CAS) waveform shows promise for enabling high range and velocity revisit rates and wideband processing gains while suppressing range ambiguities. SLO-CO is made up of non-recurrent wideband linear FM chirps that are frequency staggered according to a Costas code across the pulse repetition interval. SLO-CO is shown to provide a near-thumbtack ambiguity functions with controllable sidelobes, good Doppler and range resolution at high revisit rates. The performance of the SLO-CO waveform was tested using the Sonar Simulation Toolset (SST) as well as in the shallow water Target and Reverberation Experiment 2013 (TREX13). For both the real and simulated results, the performance of the SLO-CO is compared to the conventional CAS waveform. Amplitude-Range-Velocity (ARV) processing of SLO-CO experimental trials reveal that relatively high direct blast sidelobes mask the target peak. Methods of suppressing the direct blast are discussed including adaptive filtering and re-designing the waveform.