Optimizing PatchCore for Few/many-shot Anomaly Detection

Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and tries to distinguish between normal and anomalous data using only few selected samples. While newly proposed few-shot AD methods do compare against pre-existing algorithms developed for the full-shot domain as baselines, they do not dedicatedly optimize them for the few-shot setting. It thus remains unclear if the performance of such pre-existing algorithms can be further improved. We address said question in this work. Specifically, we present a study on the AD/anomaly segmentation (AS) performance of PatchCore, the current state-of-the-art full-shot AD/AS algorithm, in both the few-shot and the many-shot settings. We hypothesize that further performance improvements can be realized by (I) optimizing its various hyperparameters, and by (II) transferring techniques known to improve few-shot supervised learning to the AD domain. Exhaustive experiments on the public VisA and MVTec AD datasets reveal that (I) significant performance improvements can be realized by optimizing hyperparameters such as the underlying feature extractor, and that (II) image-level augmentations can, but are not guaranteed, to improve performance. Based on these findings, we achieve a new state of the art in few-shot AD on VisA, further demonstrating the merit of adapting pre-existing AD/AS methods to the few-shot setting. Last, we identify the investigation of feature extractors with a strong inductive bias as a potential future research direction for (few-shot) AD/AS

Exploring bioactivities in artemisia annua L. extracts: extraction method and solvent screening

Mestrado de dupla diplomação com a Université Libre de TunisArtemisinin, the major active ingredient in Artemisia annua L., has been used as an antimalarial ingredient and is gaining popularity for its antiviral characteristics. Furthermore, some recent articles revealed that this important molecule could be beneficial against the Sars-CoV-2 virus, brain tumours, covid-19, and other diseases. Recently, there has been a push to enhance artemisinin extraction in terms of energy costs and solvent efficiency. Therefore, various innovative procedures, such as supercritical fluid extraction, pressurized solvent extraction, microwave-assisted extraction, and ultrasound-assisted extraction, are being studied and combined with several innovative solvents. Most extraction solvents have significant toxicity, flammability, and limited selectivity, and large-scale use of these solvents has a negative impact on the environment. As a result, new extraction processes less harmful to the environment are being developed. For example, some green solvents, such as ionic liquids and deep eutectic solvents, have been proposed for artemisinin extraction. The concept of green chemistry seeks to eliminate or reduce harmful compounds in chemical applications. This report will include an evaluation of alternate extraction techniques, considering artemisinin yield, in addition to examining the potentialities of green solvents after a preliminary study with ethanol 80/20. It is essential knowledge to select the method and solvents to prepare extracts from Artemisia annua L. that will be studied in terms of several bioactivities, including antimalarial.A artemisinina, o principal ingrediente ativo da Artemisia annua L., tem sido usada como um ingrediente antimalárico e está ganhando popularidade por suas características antivirais. Além disso, alguns artigos recentes revelaram que esta importante molécula pode ser benéfica contra o vírus Sars-CoV-2, tumores cerebrais, covid-19 e outras doenças. Recentemente, houve um impulso para melhorar a extração de artemisinina em termos de custos de energia e eficiência de solventes. Portanto, vários procedimentos inovadores, como extração de fluido supercrítico, extração de solvente pressurizado, extração assistida por microondas e extração assistida por ultra-som, estão sendo estudados e combinados com vários solventes inovadores. A maioria dos solventes de extração tem toxicidade significativa, inflamabilidade e seletividade limitada, e o uso em larga escala desses solventes tem um impacto negativo no meio ambiente. Como resultado, novos processos de extração menos prejudiciais ao meio ambiente estão sendo desenvolvidos. Por exemplo, alguns solventes verdes, como líquidos iônicos e solventes eutéticos profundos, foram propostos para a extração de artemisinina. O conceito de química verde visa eliminar ou reduzir compostos nocivos em aplicações químicas. Este relatório incluirá uma avaliação de técnicas alternativas de extração, considerando o rendimento de artemisinina, além de examinar as potencialidades dos solventes verdes após um estudo preliminar com etanol 80/20. É essencial o conhecimento para selecionar o método e solventes para preparar extratos de Artemisia annua L. que serão estudados em termos de várias bioatividades, incluindo antimalárico

In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis propos- es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW- FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition opera- tion and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisa- tion; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the lo- calisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presente

A language-independent, openvocabulary system based on HMMs for recognition of ultra low resolution words

Abstract: In this paper, we introduce and evaluate a system capable of recognizing words extracted from ultra low resolution images such as those frequently embedded on web pages. The design of the system has been driven by the following constraints. First, the system has to recognize small font sizes between 6-12 points where anti-aliasing and resampling filters are applied. Such procedures add noise between adjacent characters in the words and complicate any a priori segmentation of the characters. Second, the system has to be able to recognize any words in an open vocabulary setting, potentially mixing different languages in Latin alphabet. Finally, the training procedure must be automatic, i.e. without requesting to extract, segment and label manually a large set of data. These constraints led us to an architecture based on ergodic HMMs where states are associated to the characters. We also introduce several improvements of the performance increasing the order of the emission probability estimators, including minimum and maximum width constraints on the character models and a training set consisting all possible adjacency cases of Latin characters. The proposed system is evaluated on different font sizes and families, showing good robustness for sizes down to 6 points

A language-independent, openvocabulary system based on HMMs for recognition of ultra low resolution words

ABSTRACT In this paper, we introduce and evaluate a system capable of recognizing ultra low resolution words extracted from images such as those frequently embedded on web pages. The design of the system has been driven by the following constraints. First, the system has to recognize small font sizes where antialiasing and resampling procedures have been applied. Such procedures add noise on the patterns and complicate any a priori segmentation of the characters. Second, the system has to be able to recognize any words in an open vocabulary setting, potentially mixing different languages. Finally, the training procedure must be automatic, i.e. without requesting to extract, segment and label manually a large set of data. These constraints led us to an architecture based on ergodic HMMs where states are associated to the characters. We also introduce several improvements of the performance increasing the order of the emission probability estimators and including minimum and maximum duration constraints on the character models. The proposed system is evaluated on different font sizes and families, showing good robustness for sizes down to 6 points

Optimization Using Solvent-Free Microwave Hydro-diffusion Gravity Extraction of Onion Oil from Allium cepa by Response Surface Methodology

Extraction from Allium cepa using solvent-free microwave extraction (SFME) without solvent was chosen as a method in the extraction process. The method is combined with microwave hydro-diffusion gravity (MHG) technique. In this paper, onion oil was extracted from Allium cepa using solvent-free microwave hydro-diffusion gravity extraction which is as an alternative technique to produce onion oil and it has several advantages in terms of product quality and high yield. The highest yield was obtained from this research at 100 g, 450 W and 15 min is 2.5875%. Furthermore, response surface methodology (RSM) was designed to evaluate the effects of mass of raw material (g), microwave power (W) and extraction time (min) for optimization of experimental data. Response surface methodology gave the optimum condition at 99.738 g, 465.067 W, and 17.817 min is 2.677%. The error rates between the experimental and predicted model which are less than 5% indicate that values obtained in optimal conditions correspond to theoretical values and it can be used as a reference for optimizin

Wide-Angle Multistatic Synthetic Aperture Radar: Focused Image Formation and Aliasing Artifact Mitigation

Traditional monostatic Synthetic Aperture Radar (SAR) platforms force the user to choose between two image types: larger, low resolution images or smaller, high resolution images. Switching to a Wide-Angle Multistatic Synthetic Aperture Radar (WAM-SAR) approach allows formation of large high-resolution images. Unfortunately, WAM-SAR suffers from two significant implementation problems. First, wavefront curvature effects, non-linear flight paths, and warped ground planes lead to image defocusing with traditional SAR processing methods. A new 3-D monostatic/bistatic image formation routine solves the defocusing problem, correcting for all relevant wide-angle effects. Inverse SAR (ISAR) imagery from a Radar Cross Section (RCS) chamber validates this approach. The second implementation problem stems from the large Doppler spread in the wide-angle scene, leading to severe aliasing problems. This research effort develops a new anti-aliasing technique using randomized Stepped-Frequency (SF) waveforms to form Doppler filter nulls coinciding with aliasing artifact locations. Both simulation and laboratory results demonstrate effective performance, eliminating more than 99% of the aliased energy