Detection of Illicit Drugs and Drug Precursors with Cantilever-Enhanced Photoacoustic Spectroscopy

In this study, cantilever-enhanced photoacoustic spectroscopy (CEPAS) was applied in different drug detection schemes. The study was divided into two different applications: trace detection of vaporized drugs and drug precursors in the gas-phase, and detection of cocaine abuse in hair. The main focus, however, was the study of hair samples. In the gas-phase, methyl benzoate, a hydrolysis product of cocaine hydrochloride, and benzyl methyl ketone (BMK), a precursor of amphetamine and methamphetamine were investigated. In the solid-phase, hair samples from cocaine overdose patients were measured and compared to a drug-free reference group. As hair consists mostly of long fibrous proteins generally called keratin, proteins from fingernails and saliva were also studied for comparison. Different measurement setups were applied in this study. Gas measurements were carried out using quantum cascade lasers (QLC) as a source in the photoacoustic detection. Also, an external cavity (EC) design was used for a broader tuning range. Detection limits of 3.4 particles per billion (ppb) for methyl benzoate and 26 ppb for BMK in 0.9 s were achieved with the EC-QCL PAS setup. The achieved detection limits are sufficient for realistic drug detection applications. The measurements from drug overdose patients were carried out using Fourier transform infrared (FTIR) PAS. The drug-containing hair samples and drug-free samples were both measured with the FTIR-PAS setup, and the measured spectra were analyzed statistically with principal component analysis (PCA). The two groups were separated by their spectra with PCA and proper spectral pre-processing. To improve the method, ECQCL measurements of the hair samples, and studies using photoacoustic microsampling techniques, were performed. High quality, high-resolution spectra with a broad tuning range were recorded from a single hair fiber. This broad tuning range of an EC-QCL has not previously been used in the photoacoustic spectroscopy of solids. However, no drug detection studies were performed with the EC-QCL solid-phase setup.Siirretty Doriast

Theory and algorithms for efficient physically-based illumination

Realistic image synthesis is one of the central fields of study within computer graphics. This thesis treats efficient methods for simulating light transport in situations where the incident illumination is produced by non-pointlike area light sources and distant illumination described by environment maps. We describe novel theory and algorithms for physically-based lighting computations, and expose the design choices and tradeoffs on which the techniques are based. Two publications included in this thesis deal with precomputed light transport. These techniques produce interactive renderings of static scenes under dynamic illumination and full global illumination effects. This is achieved through sacrificing the ability to freely deform and move the objects in the scene. We present a comprehensive mathematical framework for precomputed light transport. The framework, which is given as an abstract operator equation that extends the well-known rendering equation, encompasses a significant amount of prior work as its special cases. We also present a particular method for rendering objects in low-frequency lighting environments, where increased efficiency is gained through the use of compactly supported function bases. Physically-based shadows from area and environmental light sources are an important factor in perceived image realism. We present two algorithms for shadow computation. The first technique computes shadows cast by low-frequency environmental illumination on animated objects at interactive rates without requiring difficult precomputation or a priori knowledge of the animations. Here the capability to animate is gained by forfeiting indirect illumination. Another novel shadow algorithm for off-line rendering significantly enhances a previous physically-based soft shadow technique by introducing an improved spatial hierarchy that alleviates redundant computations at the cost of using more memory. This thesis advances the state of the art in realistic image synthesis by introducing several algorithms that are more efficient than their predecessors. Furthermore, the theoretical contributions should enable the transfer of ideas from one particular application to others through abstract generalization of the underlying mathematical concepts.Tämä tutkimus käsittelee realististen kuvien syntetisointia tietokoneella tilanteissa, jossa virtuaalisen ympäristön valonlähteet ovat fysikaalisesti mielekkäitä. Fysikaalisella mielekkyydellä tarkoitetaan sitä, että valonlähteet eivät ole idealisoituja eli pistemäisiä, vaan joko tavanomaisia pinta-alallisia valoja tai kaukaisia ympäristövalokenttiä (environment maps). Väitöskirjassa esitetään uusia algoritmeja, jotka soveltuvat matemaattisesti perusteltujen valaistusapproksimaatioiden laskentaan erilaisissa käyttötilanteissa. Esilaskettu valonkuljetus on yleisnimi reaaliaikaisille menetelmille, jotka tuottavat kuvia staattisista ympäristöistä siten, että valaistus voi muuttua ajon aikana vapaasti ennalta määrätyissä rajoissa. Tässä työssä esitetään esilasketulle valonkuljetukselle kattava matemaattinen kehys, joka selittää erikoistapauksinaan suuren määrän aiempaa tutkimusta. Kehys annetaan abstraktin lineaarisen operaattoriyhtälön muodossa, ja se yleistää tunnettua kuvanmuodostusyhtälöä (rendering equation). Työssä esitetään myös esilasketun valonkuljetuksen algoritmi, joka parantaa aiempien vastaavien menetelmien tehokkuutta esittämällä valaistuksen funktiokannassa, jonka ominaisuuksien vuoksi ajonaikainen laskenta vähenee huomattavasti. Fysikaalisesti mielekkäät valonlähteet tuottavat pehmeäreunaisia varjoja. Työssä esitetään uusi algoritmi pehmeiden varjojen laskemiseksi liikkuville ja muotoaan muuttaville kappaleille, joita valaisee matalataajuinen ympäristövalokenttä. Useimmista aiemmista menetelmistä poiketen algoritmi ei vaadi esitietoa siitä, kuinka kappale voi muuttaa muotoaan ajon aikana. Muodonmuutoksen aiheuttaman suuren laskentakuorman vuoksi epäsuoraa valaistusta ei huomioida. Työssä esitetään myös toinen uusi algoritmi pehmeiden varjojen laskemiseksi, jossa aiemman varjotilavuuksiin (shadow volumes) perustuvan algoritmin tehokkuutta parannetaan merkittävästi uuden hierarkkisen avaruudellisen hakurakenteen avulla. Uusi rakenne vähentää epäoleellista laskentaa muistinkulutuksen kustannuksella. Työssä esitetään aiempaa tehokkaampia algoritmeja fysikaalisesti perustellun valaistuksen laskentaan. Niiden lisäksi työn esilaskettua valonkuljetusta koskevat teoreettiset tulokset yleistävät suuren joukon aiempaa tutkimusta ja mahdollistavat näin ideoiden siirron erityisalalta toiselle.reviewe

Solving Audio Inverse Problems with a Diffusion Model

This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic setting. CQT-Diff is a neural diffusion model with an architecture that is carefully constructed to exploit pitch-equivariant symmetries in music. This is achieved by preconditioning the model with an invertible Constant-Q Transform (CQT), whose logarithmically-spaced frequency axis represents pitch equivariance as translation equivariance. The proposed method is evaluated with objective and subjective metrics in three different and varied tasks: audio bandwidth extension, inpainting, and declipping. The results show that CQT-Diff outperforms the compared baselines and ablations in audio bandwidth extension and, without retraining, delivers competitive performance against modern baselines in audio inpainting and declipping. This work represents the first diffusion-based general framework for solving inverse problems in audio processing.Comment: Submitted to ICASSP 202

GANSpace: Discovering Interpretable GAN Controls

This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied either in latent space or feature space. Then, we show that a large number of interpretable controls can be defined by layer-wise perturbation along the principal directions. Moreover, we show that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner. We show results on different GANs trained on various datasets, and demonstrate good qualitative matches to edit directions found through earlier supervised approaches.Comment: Accepted to NeurIPS 202

New economic methodology

Peer reviewe

Decoupled Sampling for Real-Time Graphics Pipelines

We propose decoupled sampling, an approach that decouples shading from visibility sampling in order to enable motion blur and depth-of-field at reduced cost. More generally, it enables extensions of modern real-time graphics pipelines that provide controllable shading rates to trade off quality for performance. It can be thought of as a generalization of GPU-style multisample antialiasing (MSAA) to support unpredictable shading rates, with arbitrary mappings from visibility to shading samples as introduced by motion blur, depth-of-field, and adaptive shading. It is inspired by the Reyes architecture in offline rendering, but targets real-time pipelines by driving shading from visibility samples as in GPUs, and removes the need for micropolygon dicing or rasterization. Decoupled Sampling works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. We present extensions of two modern GPU pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion blur and depth-of-field, as well as variable and adaptive shading rates

Decoupled Sampling for Graphics Pipelines

We propose a generalized approach to decoupling shading from visibility sampling in graphics pipelines, which we call decoupled sampling. Decoupled sampling enables stochastic supersampling of motion and defocus blur at reduced shading cost, as well as controllable or adaptive shading rates which trade off shading quality for performance. It can be thought of as a generalization of multisample antialiasing (MSAA) to support complex and dynamic mappings from visibility to shading samples, as introduced by motion and defocus blur and adaptive shading. It works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. Decoupled sampling is inspired by the Reyes rendering architecture, but like traditional graphics pipelines, it shades fragments rather than micropolygon vertices, decoupling shading from the geometry sampling rate. Also unlike Reyes, decoupled sampling only shades fragments after precise computation of visibility, reducing overshading. We present extensions of two modern graphics pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications of decoupled sampling and blur, and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion and defocus blur, as well as variable and adaptive shading rates