Context. Software testing is becoming more and more important in software development life-cycle especially for web testing. Selenium is one of the most widely used property-based Graph-User-Interface(GUI) web testing tools. Nevertheless, it also has some limitations. For instance, Selenium cannot test the web components in some specific plugins or HTML5 videos frame. But it is important for testers to verify the functionality of plugins or videos on the websites. Recently, the theory of the image recognition-based GUI testing is introduced which can locate and interact with the components to be tested on the websites by image recognition. There are only a few papers do research on comparing property-based GUI web testing and image recognition-based GUI testing. Hence, we formulated our research objectives based on this main gap. Objectives. We want to compare these two different techniques with EyeSel which is the tool represents the image recognition-based GUI testing and Selenium which is the tool represents the property-based GUI testing. We will evaluate and compare the strengths and drawbacks of these two tools by formulating specific JUnit testing scripts. Besides, we will analyze the comparative result and then evaluate if EyeSel can solve some of the limitations associated with Selenium. Therefore, we can conclude the benefits and drawbacks of property-based GUI web testing and image recognition-based GUI testing.   Methods. We conduct an experiment to develop test cases based on websites’ components both by Selenium and EyeSel. The experiment is conducted in an educational environment and we select 50 diverse websites as the subjects of the experiment. The test scripts are written in JAVA and ran by Eclipse.  The experiment data is collected for comparing and analyzing these two tools. Results. We use quantitative analysis and qualitative analysis to analyze our results. First of all, we use quantitative analysis to evaluate the effectiveness and efficiency of two GUI web testing tools. The effectiveness is measured by the number of components that can be tested by these two tools while the efficiency is measured by the measurements of test cases’ development time and execution time. The results are as follows (1) EyeSel can test more number of components in web testing than Selenium (2) Testers need more time to develop test cases by Selenium than by EyeSel (3) Selenium executes the test cases faster than EyeSel. (4) “Results (1)” indicates the effectiveness of EyeSel is better than Selenium while “Results (2)(3)” indicate the efficiency of EyeSel is better than Selenium. Secondly, we use qualitative analysis to evaluate four quality characteristics (learnability, robustness, portability, functionality) of two GUI web testing tools. The results show that portability and functionality of Selenium are better than EyeSel while the learnability of EyeSel is better than Selenium. And both of them have good robustness in web testing. Conclusions. After analyzing the results of comparison between Selenium and EyeSel, we conclude that (1) Image recognition-based GUI testing is more effectiveness than property-based GUI web testing (2) Image recognition-based GUI testing is more efficiency than property-based GUI web testing (3) The portability and functionality of property-based GUI web testing is better than Image recognition-based GUI testing (4) The learnability of image recognition-based GUI testing is better than property-based GUI web testing. (5) Both of them are good at different aspects of robustness

There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.

Face recognition is one of the most natural activities performed by the human beings. It has wide range of applications in the areas of Human Computer Interaction, Surveillance, Security etc. Face information of people can be obtained in a non-intrusive manner, without violating privacy. But, robust face recognition which is invariant under varying pose, illumination etc is still a challenging problem. The main aim of this thesis is to explore the usefulness of smile profile of human beings as an extra aid in recognizing people by faces. Smile profile of a person is the sequence of images captured by a camera when the person voluntarily smiles. Using sequence of images instead of a single image will increase the required computational resources significantly. The challenge here is to design a feature extraction technique from a smile sample, which is useful for authentication and is also efficient in terms of storage and computational aspects. There are some experimental evidences which support the claim that facial expressions have some person specific information. But, to the best of our knowledge, systematic study of a particular facial expression for biometrical purposes has not been done so far. The smile profile of human beings, which is captured under some reasonably controlled setup, is used for first time for face recognition purpose. As a first step, we applied two of the recent subspace based face classifiers on the smile samples. We were not able to obtain any conclusive results out of this experiment. Next we extracted features using only the difference vectors obtained from smile samples. The difference vectors depend only on the variations which occur in the corresponding smile profile. Hence any characterization we obtain from such features can be fully attributed to the smiling action. The feature extraction technique we employed is very much similar to PCA. The smile signature that we have obtained is named as Principal Direction of Change(PDC). PDC is a unit vector (in some high dimensional space) which represents the direction in which the major changes occurred during the smile. We obtained a reasonable recognition rate by applying Nearest Neighbor Classifier(NNC) on these features. In addition to that, these features turn out to be less sensitive to the speed of smiling action and minor variations in face detection and head orientation, while capturing the pattern of variations in various regions of face due to smiling action. Using set of experiments on PDC based features we establish that smile has some person specific characteristics. But the recognition rates of PDC based features are less than the recent conventional techniques. Next we have used PDC based features to aid a conventional face classifier. We have used smile signatures to reject some candidate faces. Our experiments show that, using smile signatures, we can reject some of the potential false candidate faces which would have been accepted by the conventional face classifier. Using this smile signature based rejection, the performance of the conventional classifier is improved significantly. This improvement suggests that, the biometric information available in smile profiles does not exist in still images. Hence the usefulness of smile profiles for biometric applications is established through this experimental investigation.

The focus of this report is to discuss graphical password systems and how they can contribute to handle security problems that threaten authentication processes. One such threat is shoulder-surfing attacks, which are also reviewed in this report. Three already existing systems that are claimed to be shoulder-surfing resilient are described and a new proposed system is presented and evaluated through a user study. Moreover, the system is compared to the mentioned existing systems to further evaluate the usability, memorability and the time it takes to authenticate. The user study shows that test subjects are able to remember their chosen password one week after having registered and signed in once. It is also shown that the average time to sign in to the system after five minutes of practice is within a range of 3.30 to 5.70 seconds. The participants in the experiments gave the system an average score above 68 on the System Usability Scale, which is the score of an average system.

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative, non-invasive and non-destructive technique useful in biological studies. By manipulating the magnetization of nuclei with non-zero spin, NMR gives insights into atomic level details. Application of NMR as a tool for discovering structure, understanding dynamics of bio-molecules such as proteins, metabolites, DNA, RNA and their interactions constitutes the field of bio-molecular NMR. In this thesis, new methods for rapid data analysis of NMR spectrum of proteins and metabolites are proposed. The first computational method, PROMEB (Pattern Recognition Based Assignment in Metabolomics) is useful for the identification and assignments of metabolites. This is an important step in metabolomics and is necessary for the discovery of new biomarkers. In NMR spectroscopy based studies, the conventional approach involves a database search, wherein chemical shifts are assigned to specific metabolites by use of a tolerance limit. This is inefficient because deviation in chemical shifts associated with pH or temperature variations, as well as missing peaks, impairs a robust comparison with the database. These drawbacks are overcome in PROMEB, which is a method based on matching the pattern of peaks of a metabolite in 2D [13C, 1H] HSQC NMR spectrum, rather than conventionally used absolute tolerance thresholds. A high success rate is obtained even in the presence of large chemical shift deviations such as 0.5 ppm in 1H and 3 ppm in 13C and missing peaks (up to 50%), compared to nearly no assignments obtained under these conditions with existing methods that employ a direct database search approach. The pattern recognition approach thus helps in identification and assignment of metabolites in-dependent of the pH, temperature, and ionic strength used, thereby obviating the need for spectral calibration with internal or external standards. Another computational method, ChemSMP(Chemical Shifts to Metabolic Path-ways), is described which facilitates the identification of metabolic pathways from a single two dimensional (2D) NMR spectrum. Typically in other approaches, this is done after relevant metabolites are identified to allow their mapping onto specific metabolic pathways. This task is daunting due to the complex nature of cellular processes and the difficulty in establishing the identity of individual metabolites. ChemSMP uses a novel indexing and scoring system comprised of a uniqueness score and a coverage score. Benchmarks show that ChemSMP has a positive prediction rate of > 90% in the presence of decluttered data and can sustain the same at 60 − 70% even in the presence of noise, such as deletions of peaks and chemical shift deviations. The method tested on NMR data acquired for a mixture of 20 amino acids shows a success rate of 93% in correct recovery of metabolic pathways. The third method developed is a new approach for rapid resonance assignments in proteins based on amino acid selective unlabeling. The method involves choosing a set of multiple amino acid types for selective unlabeling and identifying specific tripeptides surrounding the labeled residues from specific 2D NMR spectra in a combinatorial manner. The methodology directly yields sequence specific resonance assignments, without requiring a contiguously assigned stretch of amino acid residues to be linked, and is applicable to deuterated proteins. The fourth method involves a simple approach to rapidly identify amino acid types in proteins from a 2D NMR spectrum. The method is based on the fact that 13Cβ chemical shifts of different amino acid types fall in distinct spectral regions. By evolving the 13C chemical shifts in the conventional HNCACB or HN(CO)CACB type experiment for a single specified delay period, the phase of the cross peaks of different amino acid residues are modulated depending on their 13Cβ chemical shift values. Following this specified evolution period, the 2D HN projections of these experiments are acquired. The 13C evolution period can be chosen such that all residues belonging to a given set of amino acid types have the same phase pattern (positive or negative) facilitating their identification. This approach does not re-quire the preparation of any additional samples, involves the analysis of 2D [15N,1H] HSQC-type spectra obtained from the routinely used triple resonance experiments with minor modifications, and is applicable to deuterated proteins. Finally, the practical application of these methods for laboratory research is presented. PROMEB and ChemSMP is used to study cancer cell metabolism in previously unexplored oncogenic cell line. PROMEB helped in assigning a differential metabolite present at high concentration in cancer cell line compared to control non-cancerous cell line. ChemSMP revealed active metabolic pathways responsible for regulating energy homeostasis of cancer cells which were previously reported in literature. The two methods developed for rapid protein resonance assignments can be used in applications such as identifying active-site residues involved in ligand binding, phosphorylation, or protein-protein interactions. The phase modulated experiments will be useful for quick assignment of signals that shift during ligand binding or in combination with selective labeling/unlabeling approaches for identification of amino acid types to aid the sequential assignment process. Both the methodology was applied to two proteins: Ubiquitin (8 kDa) and L-IGFBP2 an intrinsically disordered protein (12 kDa), for demonstrating rapid resonance assignment using only set of 2D NMR experiments.

Rozpoznávanie tvárí je komplexný proces, ktorého hlavným ciežom je rozpoznanie žudskej tváre v obrázku alebo vo video sekvencii. Najčastejšími aplikáciami sú sledovacie a identifikačné systémy. Taktiež je rozpoznávanie tvárí dôležité vo výskume počítačového videnia a umelej inteligencií. Systémy rozpoznávania tvárí sú často založené na analýze obrazu alebo na neurónových sieťach. Táto práca sa zaoberá implementáciou algoritmu založeného na takzvaných „Eigenfaces“ tvárach. „Eigenfaces“ tváre sú výsledkom Analýzy hlavných komponent (Principal Component Analysis - PCA), ktorá extrahuje najdôležitejšie tvárové črty z originálneho obrázku. Táto metóda je založená na riešení lineárnej maticovej rovnice, kde zo známej kovariančnej matice sa počítajú takzvané „eigenvalues“ a „eigenvectors“, v preklade vlastné hodnoty a vlastné vektory. Tvár, ktorá má byť rozpoznaná, sa premietne do takzvaného „eigenspace“ (priestor vlastných hodnôt). Vlastné rozpoznanie je na základe porovnania takýchto tvárí s existujúcou databázou tvárí, ktorá je premietnutá do rovnakého „eigenspace“. Pred procesom rozpoznávania tvárí, musí byť tvár lokalizovaná v obrázku a upravená (normalizácia, kompenzácia svetelných podmienok a odstránenie šumu). Existuje mnoho algoritmov na lokalizáciu tváre, ale v tejto práci je použitý algoritmus lokalizácie tváre na základe farby žudskej pokožky, ktorý je rýchly a postačujúci pre túto aplikáciu. Algoritmy rozpoznávania tváre a lokalizácie tváre sú implementované do DSP procesoru Blackfin ADSP-BF561 od Analog Devices.

Una de las primeras etapas en la Planificación de Procesos asistida por ordenador, para procesos de mecanizado por arranque de material, consiste en identificar las zonas de material a eliminar en el bruto de partida para generar la pieza. El resultado es un conjunto de entidades llamadas: Elementos Característicos de Mecanizado, que tienen una clara relación con las operaciones de mecanizado. Al procedimiento de obtención automática de estas entidades se le denomina: reconocimiento automático de Elementos Característicos de Mecanizado (AFR, Automatic Feature Recognition), en el que partiendo del modelo 3D del bruto y de la pieza se establecen las entidades de trabajo adecuadas (Elementos Característicos de Mecanizado). Estas entidades contienen la información necesaria para poder llevar a cabo una Planificación de Procesos automática. A su vez, la información se va completando y ampliando a medida que se avanza en las etapas de la Planificación. En la Tesis se plantea el reconocimiento automático de Elementos Característicos de Mecanizado como una de las primeras etapas de la Planificación de Procesos, y que permite el enlace con el diseño asistido por ordenador. Este reconocimiento debe tener un planteamiento dinámico, ofreciendo distintas opciones. Su solución no debe ser una entrada estática, prefijada, para el resto de etapas de la Planificación. El proceso de reconocimiento está fuertemente influenciado por conceptos y decisiones de índole tecnológico (tipos de herramientas, movimientos característicos de los procesos, influencia del corte vinculado, ), que lo guían y que permiten obtener resultados válidos en la aplicación destino: el mecanizado. Atendiendo a este planteamiento, la Tesis ofrece una solución general y completa al proceso de reconocimiento automático de Elementos Característicos de Mecanizado, teniendo en cuenta a los llamados procesos convencionales (torneado, fresado, limado, rectificado, etc.). La solución propuesta no se restringe a piezas / Gutiérrez Rubert, SC. (2007). Análisis y procesado tecnológico del modelo sólido de una pieza para determinar sus elementos característicos de mecanizado [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1963 / Palancia