Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip. Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed. Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3. Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

Alphanumeric LCD infrared control via computer’s parallel port

The present work will explain a method to achieve a remote controlled (via IR LED) alphanumeric Liquid Crystal Display. In modern times, the remote access of different devices has become quite popular, therefore, the aim of this project is to provide a useful tool that will integrate common and easy to access devices. The system includes a C language based user interface, an assembly language code for the AT89C51ED2 microcontroller instructions and some digital electronic circuits needed for the driving and control of both the LCD and the infrared communication, as well as the PC with a parallel port. The interaction of all the devices provides a whole system that can be helpful in different applications, or it can be separated into each one of its different stages to take the best advantage as possible.

Cloud computing - Moving to the cloud

Cloud computing innebär användning av datorresurser som är tillgängliga via ett nätverk, oftast Internet och är ett område som har vuxit fram i snabb takt under de senaste åren. Allt fler företag migrerar hela eller delar av sin verksamhet till molnet. Sogeti i Borlänge har behov av att migrera sina utvecklingsmiljöer till en molntjänst då drift och underhåll av dessa är kostsamma och tidsödande. Som Microsoftpartners vill Sogeti använda Microsoft tjänst för cloud computing, Windows Azure, för detta syfte. Migration till molnet är ett nytt område för Sogeti och de har inga beskrivningar för hur en sådan process går till. Vårt uppdrag var att utveckla ett tillvägagångssätt för migration av en IT-lösning till molnet. En del av uppdraget blev då att kartlägga cloud computing, dess beståndsdelar samt vilka för- och nackdelar som finns, vilket har gjort att vi har fått grundläggande kunskap i ämnet. För att utveckla ett tillvägagångssätt för migration har vi utfört flera migrationer av virtuella maskiner till Windows Azure och utifrån dessa migrationer, litteraturstudier och intervjuer dragit slutsatser som mynnat ut i ett generellt tillvägagångssätt för migration till molnet. Resultatet har visat att det är svårt att göra en generell men samtidigt detaljerad beskrivning över ett tillvägagångssätt för migration, då scenariot ser olika ut beroende på vad som ska migreras och vilken typ av molntjänst som används. Vi har dock utifrån våra erfarenheter från våra migrationer, tillsammans med litteraturstudier, dokumentstudier och intervjuer lyft vår kunskap till en generell nivå. Från denna kunskap har vi sammanställt ett generellt tillvägagångssätt med större fokus på de förberedande aktiviteter som en organisation bör genomföra innan migration. Våra studier har även resulterat i en fördjupad beskrivning av cloud computing. I vår studie har vi inte sett att någon tidigare har beskrivit kritiska framgångsfaktorer i samband med cloud computing. I vårt empiriska arbete har vi dock identifierat tre kritiska framgångsfaktorer för cloud computing och i och med detta täckt upp en del av kunskapsgapet där emellan.

Ubiquitous computing in education : a SWOT analysis by students and teachers

Learning from anywhere anytime is a contemporary phenomenon in the field of education that is thought to be flexible, time and cost saving. The phenomenon is evident in the way computer technology mediates knowledge processes among learners. Computer technology is however, in some instances, faulted. There are studies that highlight drawbacks of computer technology use in learning. In this study we aimed at conducting a SWOT analysis on ubiquitous computing and computer-mediated social interaction and their affect on education. Students and teachers were interviewed on the mentioned concepts using focus group interviews. Our contribution in this study is, identifying what teachers and students perceive to be the strength, weaknesses, opportunities and threats of ubiquitous computing and computer-mediated social interaction in education. We also relate the findings with literature and present a common understanding on the SWOT of these concepts. Results show positive perceptions. Respondents revealed that ubiquitous computing and computer-mediated social interaction are important in their education due to advantages such as flexibility, efficiency in terms of cost and time, ability to acquire computer skills. Nevertheless disadvantages where also mentioned for example health effects, privacy and security issues, noise in the learning environment, to mention but a few. This paper gives suggestions on how to overcome threats mentioned.

Soft computing in biometrics : a pragmatic appraisal

The ever increasing spurt in digital crimes such as image manipulation, image tampering, signature forgery, image forgery, illegal transaction, etc. have hard pressed the demand to combat these forms of criminal activities. In this direction, biometrics - the computer-based validation of a persons' identity is becoming more and more essential particularly for high security systems. The essence of biometrics is the measurement of person’s physiological or behavioral characteristics, it enables authentication of a person’s identity. Biometric-based authentication is also becoming increasingly important in computer-based applications because the amount of sensitive data stored in such systems is growing. The new demands of biometric systems are robustness, high recognition rates, capability to handle imprecision, uncertainties of non-statistical kind and magnanimous flexibility. It is exactly here that, the role of soft computing techniques comes to play. The main aim of this write-up is to present a pragmatic view on applications of soft computing techniques in biometrics and to analyze its impact. It is found that soft computing has already made inroads in terms of individual methods or in combination. Applications of varieties of neural networks top the list followed by fuzzy logic and evolutionary algorithms. In a nutshell, the soft computing paradigms are used for biometric tasks such as feature extraction, dimensionality reduction, pattern identification, pattern mapping and the like.