Method development for the analysis of smokeless powders and organic gunshot residue by ultra performance liquid chromatography with tandem mass spectrometry

The goal of this project was to develop a rapid separation and detection method for analyzing organic compounds in smokeless powders and then test its applicability on gunshot residue (GSR) samples. In this project, a total of 20 common smokeless powder additives and their decomposition products were separated by ultra performance liquid chromatography (UPLC) and confirmed by tandem mass spectrometry (MS/MS) using multiple reaction monitoring mode (MRM). Some of the targeted compounds included diphenylamines, centralites, nitrotoluenes, nitroglycerin, and various phthalates. The compounds were ionized in the MS source using simultaneous positive and negative electrospray ionization (ESI) with negative atmospheric pressure chemical ionization (APCI) in order to detect all compounds in a single analysis. The developed UPLC/MS/MS method was applied to commercially available smokeless powders and gunshot residue samples recovered from the hands of shooters, spent cartridges, and smokeless powder retrieved from unfired cartridges. Distinct compositions were identified for smokeless powders from different manufacturers and from separate manufacturing lots. The procedure also produced specific chemical profiles when tested on gunshot residues from different manufacturers. Overall, this thesis represents the development of a rapid and reproducible procedure capable of simultaneously detecting the widest possible range of components present in organic gunshot residue.^

Detection of pitting corrosion in steel using image processing

This paper presents an image processing based detection method for detecting pitting corrosion in steel structures. High Dynamic Range (HDR) imaging has been carried out in this regard to demonstrate the effectiveness of such relatively inexpensive techniques that are of immense benefit to Non – Destructive – Tesing (NDT) community. The pitting corrosion of a steel sample in marine environment is successfully detected in this paper using the proposed methodology. It is observed, that the proposed method has a definite potential to be applied to a wider range of applications.

Development of a novel probe integrated with a micro-structured impedance sensor for the detection of breast cancer

The work described in this thesis focuses on the development of an innovative bioimpedance device for the detection of breast cancer using electrical impedance as the detection method. The ability for clinicians to detect and treat cancerous lesions as early as possible results in improved patient outcomes and can reduce the severity of the treatment the patient has to undergo. Therefore, new technology and devices are continually required to improve the specificity and sensitivity of the accepted detection methods. The gold standard for breast cancer detection is digital x-ray mammography but it has some significant downsides associated with it. The development of an adjunct technology to aid in the detection of breast cancers could represent a significant patient and economic benefit. In this project silicon substrates were pattern with two gold microelectrodes that allowed electrical impedance measurements to be recorded from intact tissue structures. These probes were tested and characterised using a range of in vitro and ex vivo experiments. The end application of this novel sensor device was in a first-in-human clinical trial. The initial results of this study showed that the silicon impedance device was capable of differentiating between normal and abnormal (benign and cancerous) breast tissue. The mean separation between the two tissue types 4,340 Ω with p < 0.001. The cancer type and grade at the site of the probe recordings was confirmed histologically and correlated with the electrical impedance measurements to determine if the different subtypes of cancer could each be differentiated. The results presented in this thesis showed that the novel impedance device demonstrated excellent electrochemical recording potential; was biocompatible with the growth of cultured cell lines and was capable of differentiating between intact biological tissues. The results outlined in this thesis demonstrate the potential feasibility of using electrical impedance for the differentiation of biological tissue samples. The novelty of this thesis is in the development of a new method of tissue determination with an application in breast cancer detection.

ADVANCED ISLANDING DETECTION METHODS FOR SINGLE PHASE GRID CONNECTED PV INVERTERS

Due to the growing concerns associated with fossil fuels, emphasis has been placed on clean and sustainable energy generation. This has resulted in the increase in Photovoltaics (PV) units being integrated into the utility system. The integration of PV units has raised some concerns for utility power systems, including the consequences of failing to detect islanding. Numerous methods for islanding detection have been introduced in literature. They can be categorized into local methods and remote methods. The local methods are categorically divided into passive and active methods. Active methods generally have smaller Non-Detection Zone (NDZ) but the injecting disturbances will slightly degrade the power quality and reliability of the power system. Slip Mode Frequency Shift Islanding Detection Method (SMS IDM) is an active method that uses positive feedback for islanding detection. In this method, the phase angle of the converter is controlled to have a sinusoidal function of the deviation of the Point of Common Coupling (PCC) voltage frequency from the nominal grid frequency. This method has a non-detection zone which means it fails to detect islanding for specific local load conditions. If the SMS IDM employs a different function other than the sinusoidal function for drifting the phase angle of the inverter, its non-detection zone could be smaller. In addition, Advanced Slip Mode Frequency Shift Islanding Detection Method (Advanced SMS IDM), which has been introduced in this thesis, eliminates the non-detection zone of the SMS IDM. In this method the parameters of SMS IDM change based on the local load impedance value. Moreover, the stability of the system is investigated by developing the dynamical equations of the system for two operation modes; grid connected and islanded mode. It is mathematically proven that for some loading conditions the nominal frequency is an unstable point and the operation frequency slides to another stable point, while for other loading conditions the nominal frequency is the only stable point of the system upon islanding occurring. Simulation and experimental results show the accuracy of the proposed methods in detection of islanding and verify the validity of the mathematical analysis.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Analysis and detection of partial discharges in an electric motor of a hybrid supercar.

The objective of this thesis was the development of a new detection method of partial discharge (PD) activity in the stator of an electrical hybrid supercar fed by a silicon carbide converter, for which detection with common methods make it very difficult to separate PD pulses from switching noise. This work focused on the analysis and detection of partial discharges making use of an antenna, a peak detector, and an oscilloscope capable of capturing the electromagnetic pulses emitted during PD activity. Validation of the proposed method was done by comparing the partial discharge inception voltage (PDIV) detected by this system with the one obtained from an optical method of proven accuracy, with different rise times and samples. Further development of this method, if proved successful on a full stator, can help increasing the overall reliability of the car, potentially allowing for real time detection of PD activity and predictive maintenance before failure of the insulation system in a hybrid vehicle.

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.

Prevalence and counts of Salmonella spp. in minimally processed vegetables in Sao Paulo, Brazil

Minimally processed vegetables (MPV) may be important vehicles of Salmonella spp. and cause disease. This study aimed at detecting and enumerating Salmonella spp. in MPV marketed in the city of Sao Paulo, Brazil. A total of 512 samples of MPV packages collected in retail stores were tested for Salmonella spp. and total coliforms and Escherichia coil as indication of the hygienic status. Salmonella spp. was detected in four samples, two using the detection method and two using the counting method, where the results were 8.8 x 10(2) CFU/g and 2.4 x 10(2) CFU/g. The serovars were Salmonella Typhimurium (three samples) and Salmonella enterica subsp. enterica O:47:z4,z23:- (one sample). Fourteen samples (2.7%) presented counts of E. coli above the maximum limit established by the Brazilian regulation for MPV (10(2) CFU/g). Therefore, tightened surveillance and effective intervention strategies are necessary in order to address consumers and governments concerns on safety of MPV. (C) 2011 Elsevier Ltd. All rights reserved.

Short-term spatial diffusion in sigma o+ o- optical molasses

We have measured the spatial diffusion of atoms in a three-dimensional sigma(+)-sigma(-) optical molasses over twenty milliseconds timescale, starting from the initial interaction of the atoms with the molasses. We find that the diffusion constants agree well with a linear model for these short time scales and also compare favourably to other studies of diffusion made over longer time scales. These measurements enable us to quantify the detection method known as freezing molasses. We discuss this method, for detecting and measuring the momentum distribution of cold atoms, which relies on the slow diffusion of atoms in optical molasses to produce a freeze-frame of the spatial distribution of the atoms. This method enables a longer interrogation interval, providing a greatly increased signal-to-noise ratio. (C) 1998 Elsevier Science B.V.

Field-swept pulsed electron paramagnetic resonance of Cr3+-doped ZBLAN fluoride glass

Field-swept pulsed electron paramagnetic resonance (EPR) spectra of a ZBLAN fluoride glass doped with a low concentration of Cr3+ are obtained using echo-detected EPR and hole-burning free induction decay detection. We review the utility of the pulsed EPR technique in generating field-swept EPR spectra, as well as some of the distorting effects that are peculiar to the pulsed detection method. The application of this technique to Cr3+-doped ZBLAN reveals that much of the broad resonance extending from g(eff) = 5.1 to g(eff) = 1.97, characteristic of X-band continuous wave EPR of Cr3+ in glasses, is absent. We attribute this largely to the variation in nutation frequencies across the spectrum that result from sites possessing large fine structure interactions. The description of the spin dynamics of such sites is complicated and we discuss some possible approaches to the simulation of the pulsed EPR spectra.

Genetic heterogeneity in familial acute myelogenous leukemia: Evidence for a second locus at chromosome 16q21-23.2

The identification of genes responsible for the rare cases of familial leukemia may afford insight into the mechanism underlying the more common sporadic occurrences. Here we test a single family with 11 relevant meioses transmitting autosomal dominant acute myelogenous leukemia (AML) and myelodysplasia for linkage to three potential candidate loci. In a different family with inherited AML, linkage to chromosome 21q22.1-22.2 was recently reported; we exclude linkage to 21q22.1-22.2, demonstrating that familial AML is a heterogeneous disease. After reviewing familial leukemia and observing anticipation in the form of a declining age of onset with each generation, we had proposed 9p21-22 and 16q22 as additional candidate loci. Whereas linkage to 9p21-22 can be excluded, the finding of a maximum two-point LOD score of 2.82 with the microsatellite marker D16S522 at a recombination fraction theta = 0 provides evidence supporting linkage to 16q22. Haplotype analysis reveals a 23.5-cM (17.9-Mb) commonly inherited region among all affected family members extending from D16S451 to D1GS289, In order to extract maximum linkage information with missing individuals, incomplete informativeness with individual markers in this interval, and possible deviance from strict autosomal dominant inheritance, we performed nonparametric linkage analysis (NPL) and found a maximum NPL statistic corresponding to a P-value of .00098, close to the maximum conditional probability of linkage expected for a pedigree with this structure. Mutational analysis in this region specifically excludes expansion of the AT-rich minisatellite repeat FRA16B fragile site and the CAG trinucleotide repeat in the E2F-4 transcription factor. The ''repeat expansion detection'' method, capable of detecting dynamic mutation associated with anticipation, more generally excludes large CAG repeat expansion as a cause of leukemia in this family.

Detecção de sectores cruzados e optimização de cobertura em redes LTE

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Validação do método de deteção de eritropoietina recombinante humana em urina humana por focalização isoelétrica

A eritropoietina (EPO) é uma substância que estimula a produção de eritrócitos, aumentando a oxigenação muscular, sendo segregada de forma natural pelo organismo e excretada na urina em baixas concentrações. Devido às suas propriedades e características, a EPO foi rapidamente introduzida no mundo do desporto, como substância ilícita, proporcionando vantagens no rendimento desportivo. No início de 2000 foi desenvolvido um método de deteção direta de EPO Recombinante (rHuEPO) em urina humana por Lasne, baseado na focalização isoelétrica (IEF) em gel de poliacrilamida, seguido de duplo blote, tendo este sido publicado e validado. Em 2002, a Agência Mundial Antidopagem (AMA) implementou este mesmo método, sendo atualmente um dos métodos oficiais utilizado pelos laboratórios acreditados pela AMA. Desta forma, o ponto de partida para a realização deste trabalho consistiu na necessidade de implementar e validar o método de referência de IEF para a deteção de rHuEPO em urina humana. O trabalho foi realizado no Laboratório de Análises e Dopagem (LAD) do Instituto do Desporto de Portugal (IDP), atual Instituto Português do Desporto e Juventude (IPDJ). O principal objetivo deste trabalho consistiu no estudo/investigação de diferentes parâmetros de validação (especificidade/seletividade; capacidade de identificação; limite de deteção; exatidão e repetibilidade), de acordo com o protocolado no Procedimento Geral interno do Laboratório de Análises de Dopagem de Lisboa (LAD). O referido método de triagem e confirmação revelou possuir características de desempenho conformes com os requisitos aplicáveis, pelo que é considerado validado e apto.