Gestão da Mudança na Implementação de Sistemas de Informação em Organizações de Saúde

O sector da saúde a nível nacional e internacional atravessa uma fase de digitalização de processos. As organizações de saúde têm por isso sido confrontadas com a realização de projectos de sistemas de informação. Sendo a sua área de actuação sensível, é essencial diminuir o impacto da mudança provocada pela implementação de sistemas de informação para a saúde, maximizando a eficiência dos sistemas de informação e minimizando o impacto negativo na prestação de cuidados de saúde. Um dos principais problemas de um processo de mudança é o impacto nos profissionais, que exercem resistência, podendo inviabilizar o projecto de sistemas de informação. Neste âmbito, surge a gestão da mudança, que pretende suportar a mudança organizacional. Tendo em conta a importância dos sistemas de informação para a actividade das organizações de saúde, pretende-se estudar o comportamento dos profissionais e da organização durante o processo, ou seja, as características dos processos da gestão de mudança em projectos de sistemas de informação de saúde. A metodologia de investigação utilizada seguiu um método qualitativo interpretativo com base num estudo de caso. A instituição escolhida foi o Centro Hospitalar de S. Francisco, Leiria. Procurou-se ainda uma confrontação dos principais resultados obtidos através da realização de um inquérito alargado a profissionais de outras organizações de saúde. Com os resultados obtidos espera-se poder auxiliar as organizações na definição de estratégias para maximizar a aceitação dos sistemas de informação e minimizar a resistência dos profissionais. Os resultados do estudo indiciam já uma tendência das organizações de saúde na aplicação de estratégias para minimizar o impacto dos processos de mudança nos projectos de sistemas de informação.

IT­-Forensisk undersökning av flyktigt minne : På Linux och Android enheter

Att kunna gör en effektiv undersökning av det flyktiga minnet är något som blir viktigare ochviktigare i IT-forensiska utredningar. Dels under Linux och Windows baserade PC installationermen också för mobila enheter i form av Android och enheter baserade andra mobila opperativsy-stem.Android använder sig av en modifierad Linux-kärna var modifikationer är för att anpassa kärnantill de speciella krav som gäller för ett mobilt operativsystem. Dessa modifikationer innefattardels meddelandehantering mellan processer men även ändringar till hur internminnet hanteras ochövervakas.Då dessa två kärnor är så pass nära besläktade kan samma grundläggande principer användas föratt dumpa och undersöka minne. Dumpningen sker via en kärn-modul vilket i den här rapportenutgörs av en programvara vid namn LiME vilken kan hantera bägge kärnorna.Analys av minnet kräver att verktygen som används har en förståelse för minneslayouten i fråga.Beroende på vilken metod verktyget använder så kan det även behövas information om olika sym-boler. Verktyget som används i det här examensarbetet heter Volatility och klarar på papperet avatt extrahera all den information som behövs för att kunna göra en korrekt undersökning.Arbetet avsåg att vidareutveckla existerande metoder för analys av det flyktiga minnet på Linux-baserade maskiner (PC) och inbyggda system(Android). Problem uppstod då undersökning avflyktigt minne på Android och satta mål kunde inte uppnås fullt ut. Det visade sig att minnesanalysriktat emot PC-plattformen är både enklare och smidigare än vad det är mot Android.

Digital spiral analysis for objective assessment of fine motor timing variability in Parkinson's disease

OBJECTIVES: To develop a method for objective assessment of fine motor timing variability in Parkinson’s disease (PD) patients, using digital spiral data gathered by a touch screen device. BACKGROUND: A retrospective analysis was conducted on data from 105 subjects including65 patients with advanced PD (group A), 15 intermediate patients experiencing motor fluctuations (group I), 15 early stage patients (group S), and 10 healthy elderly subjects (HE) were examined. The subjects were asked to perform repeated upper limb motor tasks by tracing a pre-drawn Archimedes spiral as shown on the screen of the device. The spiral tracing test was performed using an ergonomic pen stylus, using dominant hand. The test was repeated three times per test occasion and the subjects were instructed to complete it within 10 seconds. Digital spiral data including stylus position (x-ycoordinates) and timestamps (milliseconds) were collected and used in subsequent analysis. The total number of observations with the test battery were as follows: Swedish group (n=10079), Italian I group (n=822), Italian S group (n = 811), and HE (n=299). METHODS: The raw spiral data were processed with three data processing methods. To quantify motor timing variability during spiral drawing tasks Approximate Entropy (APEN) method was applied on digitized spiral data. APEN is designed to capture the amount of irregularity or complexity in time series. APEN requires determination of two parameters, namely, the window size and similarity measure. In our work and after experimentation, window size was set to 4 and similarity measure to 0.2 (20% of the standard deviation of the time series). The final score obtained by APEN was normalized by total drawing completion time and used in subsequent analysis. The score generated by this method is hence on denoted APEN. In addition, two more methods were applied on digital spiral data and their scores were used in subsequent analysis. The first method was based on Digital Wavelet Transform and Principal Component Analysis and generated a score representing spiral drawing impairment. The score generated by this method is hence on denoted WAV. The second method was based on standard deviation of frequency filtered drawing velocity. The score generated by this method is hence on denoted SDDV. Linear mixed-effects (LME) models were used to evaluate mean differences of the spiral scores of the three methods across the four subject groups. Test-retest reliability of the three scores was assessed after taking mean of the three possible correlations (Spearman’s rank coefficients) between the three test trials. Internal consistency of the methods was assessed by calculating correlations between their scores. RESULTS: When comparing mean spiral scores between the four subject groups, the APEN scores were different between HE subjects and three patient groups (P=0.626 for S group with 9.9% mean value difference, P=0.089 for I group with 30.2%, and P=0.0019 for A group with 44.1%). However, there were no significant differences in mean scores of the other two methods, except for the WAV between the HE and A groups (P<0.001). WAV and SDDV were highly and significantly correlated to each other with a coefficient of 0.69. However, APEN was not correlated to neither WAV nor SDDV with coefficients of 0.11 and 0.12, respectively. Test-retest reliability coefficients of the three scores were as follows: APEN (0.9), WAV(0.83) and SD-DV (0.55). CONCLUSIONS: The results show that the digital spiral analysis-based objective APEN measure is able to significantly differentiate the healthy subjects from patients at advanced level. In contrast to the other two methods (WAV and SDDV) that are designed to quantify dyskinesias (over-medications), this method can be useful for characterizing Off symptoms in PD. The APEN was not correlated to none of the other two methods indicating that it measures a different construct of upper limb motor function in PD patients than WAV and SDDV. The APEN also had a better test-retest reliability indicating that it is more stable and consistent over time than WAV and SDDV.

Transmission network expansion planning considering phase-shifter transformers

This paper presents a novel mathematical model for the transmission network expansion planning problem. Main idea is to consider phase-shifter (PS) transformers as a new element of the transmission system expansion together with other traditional components such as transmission lines and conventional transformers. In this way, PS are added in order to redistribute active power flows in the system and, consequently, to diminish the total investment costs due to new transmission lines. Proposed mathematical model presents the structure of a mixed-integer nonlinear programming (MINLP) problem and is based on the standard DC model. In this paper, there is also applied a specialized genetic algorithm aimed at optimizing the allocation of candidate components in the network. Results obtained from computational simulations carried out with IEEE-24 bus system show an outstanding performance of the proposed methodology and model, indicating the technical viability of using these nonconventional devices during the planning process. Copyright © 2012 Celso T. Miasaki et al.

A Comparison of Induction Motor Starting Methods Being Powered by a Diesel-Generator Set

Starting induction motors on isolated or weak systems is a highly dynamic process that can cause motor and load damage as well as electrical network fluctuations. Mechanical damage is associated with the high starting current drawn by a ramping induction motor. In order to compensate the load increase, the voltage of the electrical system decreases. Different starting methods can be applied to the electrical system to reduce these and other starting method issues. The purpose of this thesis is to build accurate and usable simulation models that can aid the designer in making the choice of an appropriate motor starting method. The specific case addressed is the situation where a diesel-generator set is used as the electrical supplied source to the induction motor. The most commonly used starting methods equivalent models are simulated and compared to each other. The main contributions of this thesis is that motor dynamic impedance is continuously calculated and fed back to the generator model to simulate the coupling of the electrical system. The comparative analysis given by the simulations has shown reasonably similar characteristics to other comparative studies. The diesel-generator and induction motor simulations have shown good results, and can adequately demonstrate the dynamics for testing and comparing the starting methods. Further work is suggested to refine the equivalent impedance presented in this thesis.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.

Designing Software Product Lines for Testability

Software product line (SPL) engineering offers several advantages in the development of families of software products such as reduced costs, high quality and a short time to market. A software product line is a set of software intensive systems, each of which shares a common core set of functionalities, but also differs from the other products through customization tailored to fit the needs of individual groups of customers. The differences between products within the family are well-understood and organized into a feature model that represents the variability of the SPL. Products can then be built by generating and composing features described in the feature model. Testing of software product lines has become a bottleneck in the SPL development lifecycle, since many of the techniques used in their testing have been borrowed from traditional software testing and do not directly take advantage of the similarities between products. This limits the overall gains that can be achieved in SPL engineering. Recent work proposed by both industry and the research community for improving SPL testing has begun to consider this problem, but there is still a need for better testing techniques that are tailored to SPL development. In this thesis, I make two primary contributions to software product line testing. First I propose a new definition for testability of SPLs that is based on the ability to re-use test cases between products without a loss of fault detection effectiveness. I build on this idea to identify elements of the feature model that contribute positively and/or negatively towards SPL testability. Second, I provide a graph based testing approach called the FIG Basis Path method that selects products and features for testing based on a feature dependency graph. This method should increase our ability to re-use results of test cases across successive products in the family and reduce testing effort. I report the results of a case study involving several non-trivial SPLs and show that for these objects, the FIG Basis Path method is as effective as testing all products, but requires us to test no more than 24% of the products in the SPL.

Classificazione di oggetti in immagini attraverso il modello Bag of Visual Words

Generic object recognition is an important function of the human visual system and everybody finds it highly useful in their everyday life. For an artificial vision system it is a really hard, complex and challenging task because instances of the same object category can generate very different images, depending of different variables such as illumination conditions, the pose of an object, the viewpoint of the camera, partial occlusions, and unrelated background clutter. The purpose of this thesis is to develop a system that is able to classify objects in 2D images based on the context, and identify to which category the object belongs to. Given an image, the system can classify it and decide the correct categorie of the object. Furthermore the objective of this thesis is also to test the performance and the precision of different supervised Machine Learning algorithms in this specific task of object image categorization. Through different experiments the implemented application reveals good categorization performances despite the difficulty of the problem. However this project is open to future improvement; it is possible to implement new algorithms that has not been invented yet or using other techniques to extract features to make the system more reliable. This application can be installed inside an embedded system and after trained (performed outside the system), so it can become able to classify objects in a real-time. The information given from a 3D stereocamera, developed inside the department of Computer Engineering of the University of Bologna, can be used to improve the accuracy of the classification task. The idea is to segment a single object in a scene using the depth given from a stereocamera and in this way make the classification more accurate.

An integrated environment for filter design

Analog filters and direct digital filters are implemented using digital signal processing techniques. Specifically, Butterworth, Elliptic, and Chebyshev filters are implemented using the Motorola 56001 Digital Signal Processor by the integration of three software packages: MATLAB, C++, and Motorola's Application Development System. The integrated environment allows the novice user to design a filter automatically by specifying the filter order and critical frequencies, while permitting more experienced designers to take advantage of MATLAB's advanced design capabilities. This project bridges the gap between the theoretical results produced by MATLAB and the practicalities of implementing digital filters using the Motorola 56001 Digital Signal Processor. While these results are specific to the Motorola 56001 they may be extended to other digital signal processors. MATLAB handles the filter calculations, a C++ routine handles the conversion to assembly code, and the Motorola software compiles and transmits the code to the processor

Exploring Engineering

Engineering faculty members at Bucknell University have established a course required for freshman engineering students and open to liberal arts students. The course has been designed to stimulate and enhance student interest in all the engineering disciplines at Bucknell. The course ranges broadly across small groups, faculty-lead recitations, laboratory experiences, student design projects, traditional lectures, and guest speakers. The exploring engineering course has completed its second year. The authors describe the course, the changes made since the initial offering and the impact on the students and faculty involved. They also present and interpret student evaluations of the course.(4 refs)

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Design and implementation of a 3D computer game controller using inertial MEMS sensors

Though 3D computer graphics has seen tremendous advancement in the past two decades, most available mechanisms for computer interaction in 3D are high cost and targeted for industry and virtual reality applications. Recent advances in Micro-Electro-Mechanical-System (MEMS) devices have brought forth a variety of new low-cost, low-power, miniature sensors with high accuracy, which are well suited for hand-held devices. In this work a novel design for a 3D computer game controller using inertial sensors is proposed, and a prototype device based on this design is implemented. The design incorporates MEMS accelerometers and gyroscopes from Analog Devices to measure the three components of the acceleration and angular velocity. From these sensor readings, the position and orientation of the hand-held compartment can be calculated using numerical methods. The implemented prototype is utilizes a USB 2.0 compliant interface for power and communication with the host system. A Microchip dsPIC microcontroller is used in the design. This microcontroller integrates the analog to digital converters, the program memory flash, as well as the core processor, on a single integrated circuit. A PC running Microsoft Windows operating system is used as the host machine. Prototype firmware for the microcontroller is developed and tested to establish the communication between the design and the host, and perform the data acquisition and initial filtering of the sensor data. A PC front-end application with a graphical interface is developed to communicate with the device, and allow real-time visualization of the acquired data.

ATP modeling of internal transformer faults for relay performance testing

Transformers are very important elements of any power system. Unfortunately, they are subjected to through-faults and abnormal operating conditions which can affect not only the transformer itself but also other equipment connected to the transformer. Thus, it is essential to provide sufficient protection for transformers as well as the best possible selectivity and sensitivity of the protection. Nowadays microprocessor-based relays are widely used to protect power equipment. Current differential and voltage protection strategies are used in transformer protection applications and provide fast and sensitive multi-level protection and monitoring. The elements responsible for detecting turn-to-turn and turn-to-ground faults are the negative-sequence percentage differential element and restricted earth-fault (REF) element, respectively. During severe internal faults current transformers can saturate and slow down the speed of relay operation which affects the degree of equipment damage. The scope of this work is to develop a modeling methodology to perform simulations and laboratory tests for internal faults such as turn-to-turn and turn-to-ground for two step-down power transformers with capacity ratings of 11.2 MVA and 290 MVA. The simulated current waveforms are injected to a microprocessor relay to check its sensitivity for these internal faults. Saturation of current transformers is also studied in this work. All simulations are performed with the Alternative Transients Program (ATP) utilizing the internal fault model for three-phase two-winding transformers. The tested microprocessor relay is the SEL-487E current differential and voltage protection relay. The results showed that the ATP internal fault model can be used for testing microprocessor relays for any percentage of turns involved in an internal fault. An interesting observation from the experiments was that the SEL-487E relay is more sensitive to turn-to-turn faults than advertized for the transformers studied. The sensitivity of the restricted earth-fault element was confirmed. CT saturation cases showed that low accuracy CTs can be saturated with a high percentage of turn-to-turn faults, where the CT burden will affect the extent of saturation. Recommendations for future work include more accurate simulation of internal faults, transformer energization inrush, and other scenarios involving core saturation, using the newest version of the internal fault model. The SEL-487E relay or other microprocessor relays should again be tested for performance. Also, application of a grounding bank to the delta-connected side of a transformer will increase the zone of protection and relay performance can be tested for internal ground faults on both sides of a transformer.