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  • 1.
    Dougherty, Mark
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Slutrapport för projektet E-MOTIONS2013Report (Other academic)
  • 2.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    SVM based traffic sign classification using legender moments2007In: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007, 2007, p. 957-968Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel approach to recognize traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36×36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition. Copyright © 2007 IICAI.

  • 3.
    Hedlund, Niklas
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    IT­-Forensisk undersökning av flyktigt minne: På Linux och Android enheter2013Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The ability to be able to make a efficient investigation of volatile memory is something that getsmore and more important in IT forensic investigations. Partially for Linux and Windows based PCsystems but also for mobile devices in the form of the Android or devices based on other mobileoperative systems.Android uses a modified Linux kernel where the modifications exclusively are to adapt it to thedemands that exists in a operative system targeting mobile devices. These modifications containsmessage passing systems between processes as well as changes to the memory subsystems in theaspect of handling and monitoring.Since these two kernels are so closely related it is possible to use the same basic principles for dum-ping and analysing of the memory. The actual memory dumping is done by a kernel module whichin this report is done by the software called LiME which handles both kernels very well.Tools used to analyse the memory needs to understand the memory layout used on the systemin question, depending on the type of analyse method used it might also need information aboutthe different symbols involved. The tool used in this project is called Volatility which in theory iscapable of extracting all the information needed in order to make a correct investigation.The purpose was to expand on existing methods for analysing volatile memory on Linux-basedsystems, in the form of PC machines as well as embedded systems like Android. Difficulties arisedwhen the analysing of volatile memory for Android could not be completed according to existinggoals. The final result came to show that memory analysis targeting the PC platform is bothsimpler and more straight forward then what it is if Android is involved.

  • 4.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Triggering Radar Speed Warning signs using Association Rules and Clustering Techniques2012Conference paper (Refereed)
    Abstract [en]

    Radar speed warning signs (RSWS) have been used in recent years across Sweden and elsewhere in the world. Such signs measure vehicle speed using radar and are designed to display a message when the driver exceeds a pre-set threshold speed, which is often relative to the speed limit on a particular road segment. RSWS are typically placed on locations which are perceived to be problematic by relevant authorities. Excessive speeding or road accidents are examples of such perceived problems.  Deploying RSWS in many relevant locations is often impractical due to the lack of necessary power supply needed for operation. Battery driven RSWS are an alternative but are less attractive because of limited running time and frequent maintenance (changing batteries etc). Therefore, solar powered RSWS are more desirable. However, these signs are also dependent on batteries that need to be charged. The duration of operation of solar powered RSWS largely depend on how often the sign is triggered. Constant activation of the sign drains the battery. It is desirable to trigger the sign only when necessary. Hence, the main goal of this research is to design a model that optimises the performance of RSWS depending on prevailing conditions i.e traffic flows during different times of the day and so on.  Vehicle speed data had been collected at a test site in Sweden all hours of the day. This paper attempts to use a hybrid system based on Apriori and K-means clustering algorithm. Apriori algorithm is simple and efficient to determine associations’ rules among attributes in particular to discover the most common combination that can occur within the data set.  K-means clustering is basically used to quantize the input variables into smaller clusters that can easily derive the trigger threshold value. The proposed hybrid system indicated that the system was able to trigger solar RWWS efficiently.

  • 5.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Speed prediction for triggering vehicle activated signs2016Report (Other academic)
    Abstract [en]

    Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

  • 6.
    Jusufi, Ilir
    et al.
    Department of Computer Science, University of California Davis, CA, USA.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Visualization of spiral drawing data of patients with Parkinson's disease2014In: IEEE International Conference on Information Visualization, IEEE Press, 2014, p. 346-350Conference paper (Refereed)
    Abstract [en]

    Patients with Parkinson's disease (PD) need to be frequently monitored in order to assess their individual symptoms and treatment-related complications. Advances in technology have introduced telemedicine for patients in remote locations. However, data produced in such settings lack much information and are not easy to analyze or interpret compared to traditional, direct contact between the patient and clinician. Therefore, there is a need to present the data using visualization techniques in order to communicate in an understandable and objective manner to the clinician. This paper presents interaction and visualization approaches used to aid clinicians in the analysis of repeated measures of spirography of PD patients gathered by means of a telemetry touch screen device. The proposed approach enables clinicians to observe fine motor impairments and identify motor fluctuations of their patients while they perform the tests from their homes using the telemetry device.

  • 7.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Song, William Wei
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A case study in healthcare informatics: a telemedicine framework for automated parkinson’s disease symptom assessment2014In: Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings / [ed] Zheng X. et al., Springer, 2014, p. 197-199Conference paper (Refereed)
    Abstract [en]

    This paper reports the development and evaluation of a mobile-based telemedicine framework for enabling remote monitoring of Parkinson’s disease (PD) symptoms. The system consists of different measurement devices for remote collection, processing and presentation of symptom data of advanced PD patients. Different numerical analysis techniques were applied on the raw symptom data to extract clinically symptom information which in turn were then used in a machine learning process to be mapped to the standard clinician-based measures. The methods for quantitative and automatic assessment of symptoms were then evaluated for their clinimetric properties such as validity, reliability and sensitivity to change. Results from several studies indicate that the methods had good metrics suggesting that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients.

  • 8.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Motion cue analysis for parkinsonian gait recognition2013In: The open biomedical engineering journal, ISSN 1874-1207, Vol. 7, p. 1-8Article in journal (Refereed)
    Abstract [en]

    This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

  • 9.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A mobile-based system can assess Parkinson's disease symptoms from home environments of patients2014In: Neurologi i Sverige, ISSN 2000-8538, no 3, p. 5p. 24-28Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    Treatment of Parkinson's disease (PD) patients involves major challenges like the large within- and between-patient variability in symptom profiles and the emergence of motor complications. As PD progresses, the symptoms develop slowly and they represent a significant source of disability in advanced patients. During evaluation of treatments and symptoms, both the physician- and patient-oriented outcomes offer complementary information. In addition, quantitative assessments of symptoms using sensing technologies can potentially complement and enhance both patient and clinician perspectives. At Högskolan Dalarna, the Lecturer Mevludin Memedi has developed a telemetry system that assesses symptoms via analysis of self-assessments and motor tests to objectively measure disease-related outcomes and to improve the management of PD.

  • 10.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Aghanavesi, Somayeh
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Digital spiral analysis for objective assessment of fine motor timing variability in Parkinson's disease2015Conference paper (Other academic)
    Abstract [en]

    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.

  • 11.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Aghanavesi, Somayeh
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Objective quantification of Parkinson's disease upper limb motor timing variability using spirography2015Conference paper (Refereed)
    Abstract [en]

    Objective quantification of the upper limb motor timing variability of Parkinson’s disease (PD) patients was evaluated using traces of spirals by groups of patients at different disease stages, stable (S), intermediate (I), advanced (A) and a healthy elderly (HE) group. The approximate entropy (APEN) method of quantifying motor timing variability in time series was applied to capture the amount of irregularity during the spiral drawing process. The APEN score was then normalized by total drawing completion time and used in subsequent analysis. In addition, two previously published methods (WAV and SDDV) were applied on the spiral data. Comparing subject groups’ APEN mean scores, they were found to be significantly different from HE group, for group A (P<0.001) indicating this method’s ability in distinguishing patients at advanced disease stage. Comparing the three methods’ ability to track response to advanced treatment, APEN scores were all significantly different between base-line and levodopa-carbidopa intestinal gel (LCIG) treatment, during the 36 month study period as opposed to WAV and SDDV as they were not significantly improving for all periods. APEN scores were weakly correlated to WAV and SDDV, indicating that they measure different aspects of symptom severity.

  • 12.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet, Stockholm, Sweden.
    Pålhagen, Sven
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Willows, Thomas
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Widner, Håkan
    Department of Neurology, Skåne University Hospital, Lund, Sweden.
    Linder, Jan
    Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Validity and responsiveness of at-home touch-screen assessments in advanced Parkinson's disease2015In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 6, p. 1829-1834Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to investigate if a telemetry test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression in patients with fluctuations. Sixty-five patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study; 35 treated with levodopa-carbidopa intestinal gel (LCIG) and 30 were candidates for switching from oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments of symptoms and fine motor tests (tapping and spiral drawings), four times per day in their homes during week-long test periods. The repeated measurements were summarized into an overall test score (OTS) to represent the global condition of the patient during a test period. Clinical assessments included ratings on Unified PD Rating Scale (UPDRS) and 39-item PD Questionnaire (PDQ-39) scales. In LCIG-naïve patients, mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. In LCIG-non-naïve patients, there were no significant changes in mean OTS until month 36. The OTS correlated adequately with total UPDRS (rho = 0.59) and total PDQ-39 (0.59). Responsiveness measured as effect size was 0.696 and 0.536 for OTS and UPDRS respectively. The trends of the test scores were similar to the trends of clinical rating scores but dropout rate was high. Correlations between OTS and clinical rating scales were adequate indicating that the test battery contains important elements of the information of well-established scales. The responsiveness and reproducibility were better for OTS than for total UPDRS.

  • 13.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Sadikov, Aleksander
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Žabkar, Jure
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Možina, Martin
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Sahlgrenska Academy, Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Neurology, Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Dietrich
    NINDS Intramural Research Program, Clinical Trials Unit, National Institutes of Health, Bethesda, MD, USA.
    Nyholm, Dag
    Neurology, Neuroscience, Uppsala University, Uppsala, Sweden.
    Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease2015In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 9, p. 23727-23744Article in journal (Refereed)
    Abstract [en]

    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.

  • 14.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Sadikov, Aleksander
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Žabkar, Jure
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Možina, Martin
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Sahlgrenska Academy, Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Neurology, Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Dietrich
    NINDS Intramural Research Program, Clinical Trials Unit, National Institutes of Health, Bethesda, MD, USA.
    Nyholm, Dag
    Neurology, Neuroscience, Uppsala University, Uppsala, Sweden.
    Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease2015Conference paper (Other academic)
    Abstract [en]

    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.

  • 15.
    Meng, Xiangli
    Dalarna University, School of Technology and Business Studies, Statistics.
    Statistical bound of genetic solutions to quadratic assignment problems2015Report (Other academic)
    Abstract [en]

    Quadratic assignment problems (QAPs) are commonly solved by heuristic methods, where the optimum is sought iteratively. Heuristics are known to provide good solutions but the quality of the solutions, i.e., the confidence interval of the solution is unknown. This paper uses statistical optimum estimation techniques (SOETs) to assess the quality of Genetic algorithm solutions for QAPs. We examine the functioning of different SOETs regarding biasness, coverage rate and length of interval, and then we compare the SOET lower bound with deterministic ones. The commonly used deterministic bounds are confined to only a few algorithms. We show that, the Jackknife estimators have better performance than Weibull estimators, and when the number of heuristic solutions is as large as 100, higher order JK-estimators perform better than lower order ones. Compared with the deterministic bounds, the SOET lower bound performs significantly better than most deterministic lower bounds and is comparable with the best deterministic ones. 

  • 16. Rahman, Asif
    et al.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Simulation and optimisation techniques for sawmill yard operation: A literature review2014In: Journal of Intelligent Learning Systems and Applications, ISSN 2150-8410, Vol. 6, no 1, p. 21-34Article in journal (Refereed)
    Abstract [en]

    Increasing costs and competitive business strategies are pushing sawmill enterprises to make an effort for optimization of their process management. Organizational decisions mainly concentrate on performance and reduction of operational costs in order to maintain profit margins. Although many efforts have been made, effective utilization of resources, optimal planning and maximum productivity in sawmill are still challenging to sawmill industries. Many researchers proposed the simulation models in combination with optimization techniques to address problems of integrated logistics optimization. The combination of simulation and optimization technique identifies the optimal strategy by simulating all complex behaviours of the system under consideration including objectives and constraints. During the past decade, an enormous number of studies were conducted to simulate operational inefficiencies in order to find optimal solutions. This paper gives a review on recent developments and challenges associated with simulation and optimization techniques. It was believed that the review would provide a perfect ground to the authors in pursuing further work in optimizing sawmill yard operations.

  • 17.
    Ritola, Richard
    Dalarna University.
    Forensisk undersökning av Solid State Drive2012Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Solid State Drives (SSD) are relatively new and not much is known about them. This thesis focuses on retrieving forensically important information, not only from the storage area of the SSD but also from the spare area. This was attempted by writing a program in C++ that, using ATA commands, could read information from the SSD. Although the program was not finished within the given time, it could read some information from the SSD, but not the spare area which was the main focus.

  • 18.
    Sadikov, Aleksander
    et al.
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Žabkar, Jure
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Možina, Martin
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Groznik, Vida
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Feasibility of spirography features for objective assessment of motor symptoms in Parkinson's disease2015In: Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings / [ed] John Holmes, Riccardo Bellazzi, Lucia Sacchi and Niels Peek, Springer, 2015, Vol. 9105, p. 267-276Conference paper (Refereed)
    Abstract [en]

    Parkinsons disease (PD) is currently incurable, however the proper treatment can ease the symptoms and significantly improve the quality of patients life. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper is to investigate the feasibility of using the features and methodology of a spirography device, originally designed to measure early Parkinsons disease (PD) symptoms, for assessing motor symptoms of advanced PD patients suffering from motor fluctuations. More specifically, the aim is to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication). The work combines spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The target outcome was to learn to predict the “cause” of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. Using the machine learning methods with feature descriptions from the Slovenian application resulted in 86% classification accuracy and over 90% AUC, demonstrating the usefulness of this approach for objective monitoring of PD patients.

  • 19.
    Shah, Syed Fawad Ali
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Intelligent Algorithms for a Hybrid FuelCell/Photovoltaic Standalone System: Simulation Of Hybrid FuelCell/Photovoltaic Standalone System2010Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The Intelligent Algorithm is designed for theusing a Battery source. The main function is to automate the Hybrid System through anintelligent Algorithm so that it takes the decision according to the environmental conditionsfor utilizing the Photovoltaic/Solar Energy and in the absence of this, Fuel Cell energy isused. To enhance the performance of the Fuel Cell and Photovoltaic Cell we used batterybank which acts like a buffer and supply the current continuous to the load.

    To develop the main System whlogic based controller was used. Fuzzy Logic based controller used to develop this system,because they are chosen to be feasible for both controlling the decision process and predictingthe availability of the available energy on the basis of current Photovoltaic and Battery conditions.

    The Intelligent Algorithm is designed to optimize the performance of the system and to selectthe best available energy source(s) in regard of the input parameters. The enhance function of these Intelligent Controller is to predict the use of available energy resources and turn on thatparticular source for efficient energy utilization. A fuzzy controller was chosen to take thedecisions for the efficient energy utilization from the given resources. The fuzzy logic basedcontroller is designed in the Matlab-Simulink environment. Initially, the fuzzy based ruleswere built. Then MATLAB based simulation system was designed and implemented. Thenthis whole proposed model is simulated and tested for the accuracy of design and performanceof the system.

  • 20.
    Stefan, Vlad
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Discrete Event Simulation of a Sawmill Yard Using Multi-Agent System2011Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    In direct reference to the saying “time is money”, nowadays scenario simulations play a key role in the tasks people perform. Optimizing the time length of tasks and synchronizing them properly is essential to increased profits in any line of business.

    In this thesis the Bergkvist-Insjön sawmill yard process will be computer simulated. As the number of trucks arriving at the sawmill is unknown, the unexpected arrival of trucks would produce a high pressure on internal resources and handling operations. The aim of this paper is to optimize the usage of the resources in the Bergkvist-Insjön sawmill, by running three different scenarios built based on the real system simulation.

    Scenario number three, in which a log stacker only has the tasks to unload the trucks and supply the measurement station, has been found most efficient. In the simulation of this scenario, the number of logs processed by the sawmill is the highest one. Also, the time spent by the log stackers between their tasks is the shortest one from all scenarios.

    The results of this thesis revealed that the most efficient improvement of the sawmill yard would be gained by a different tasks’ priority for the operating log stackers.

  • 21.
    Westberg, Sofia
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Forensiska Undersökningar av Molntjänster2012Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The usage of cloud services has made forensics investigations more complicated. But there are good foundations if the cloud service providers would create services to retrieve all the information. It would make the process easier and more reliable.

    The most difficult part to do correctly is to download the information from the cloud services. The investigation is done in a volatile environment and not on a secured copy. It is possible that changes are made during the time the data is retrieved, which is not always visible. It is not possible to compare the differences in files with hash values, in the same way as forensic investigations of computers. That is why it is very important to document how the information is retrieved, preferably by recording the computer screen during the time the information is retrieved.

    The information is saved on multiple locations when the cloud services Office 365 and Google Apps are used, both in the cloud and on the computer that is being used to access the cloud. The web browser saves a lot of information of what has been done. That is why it is important to find out which computer has been used to connect to the cloud service, which is not possible today. If it would be possible to examine all the computer that have been used, evidence that is no longer in the cloud could be found,

    The best through a forensic angle would be if the cloud service providers offered to retrieve all data which involves a user, including all relevant logs. Then it would be possible to retrieve the data with a secure method, because it would not be possible to change the information during the retrieval. 

  • 22.
    Westin, Jerker
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    Pålhagen, Sven
    Willows, Thomas
    Groth, Torgny
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Karlsson, Mats
    A pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion2011In: Clinical neuropharmacology, ISSN 0362-5664, E-ISSN 1537-162X, Vol. 32, no 2, p. 61-65Article in journal (Refereed)
    Abstract [en]

    Objective: The purpose of this work was to identify and estimate a population pharmacokinetic- pharmacodynamic model for duodenal infusion of a levodopa/carbidopa gel (Duodopa) to examine pharmacological properties of this treatment.

    Methods: The modeling involved pooling data from 3 studies (on advanced Parkinson disease) and fixing some parameters to values found in literature. The first study involved 12 patients studied on 3 occasions each and was previously published. The second study involved 3 patients on 2 occasions. A bolus dose was given after a washout during night. Plasma samples and motor ratings (clinical assessment of motor function on a 7-point treatment response scale ranging from "very off" to "very hyperkinetic") were collected until the clinical effect returned to baseline. The third study involved 5 patients on 3 occasions receiving 5 different dose levels. Different structural models were evaluated using the nonlinear mixed-effects modeling program NONMEM VI. Population mean parameter values, and interindividual, interoccasion, and residual variabilities were estimated.

    Results: Absorption of the levodopa/carbidopa gel can be adequately described with first-order absorption with bioavailability and lag time. Estimated population parameter values were a mean absorption time of 28.5 minutes, a lag time of 2.9 minutes, and a bioavailability of 88%. The pharmacodynamic model for motor ratings had the following population values: a half-life of effect delay of 21 minutes, a concentration at 50% effect of 1.55 mg/L, an Emax of 2.39 U on the treatment response scale, and a sigmoidicity of the Emax function of 11.6.

    Conclusions: For the typical unmedicated subject, it will take 51.4 minutes until the peak levodopa effect is reached after a bolus dose. This delay is, like the magnitude of the effect, highly variable in this patient group. The residual error magnitudes of 20% for levodopa concentrations and 0.92 U (SD) for motor ratings indicate that the models developed provide predictions of a relevant quality. The developed model may be a first step toward model-guided treatment individualization of duodenal infusion of levodopa.

  • 23.
    Yella, Siril
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Automatically detecting the number of logs on a timber truck2013In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 22, no 4, p. 417-435Article in journal (Refereed)
    Abstract [en]

    This article describes a method of automatically detecting, counting and classifying logs on a timber truck using a photograph (taken by the driver). An image-processing algorithm is developed to process the photograph to calculate an estimate of the number of logs present and their respective diameters. The algorithm uses color information in multiple color spaces as well as geometrical operators to segment the image and extract the relevant information. This information enables the sawmill to better plan internal logistics and production in advance of the truck’s arrival time. The algorithm is robust with respect to external factors such as varying lighting conditions and camera angle, but some inaccuracies remain, mainly caused by logs being occluded or covered in mud or snow.

  • 24.
    Yella, Siril
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Gupta, Narendra
    Fuzzy logic approach for automating visual condition monitoring of railway sleepers2007In: IICAI, Indian International Conference on Artificial Intelligence, Pune, India, 2007Conference paper (Other academic)
  • 25.
    Yella, Siril
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Gupta, Naredra K.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Automating condition monitoring of wooden railway sleepers2007In: EngineerIT, ISSN 1991-5047, Vol. 2, no 10Article in journal (Other academic)
  • 26.
    Yella, Siril
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Shaik, Asif ur Rahman
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Pattern recognition for classifying the condition of wooden railway sleepers2010In: Multimedia Computing and Information Technology (MCIT), 2010 International Conference on Multimedia Computing and Information Technology, Sharjah, 2010, p. 61-64Conference paper (Refereed)
    Abstract [en]

    This paper summarises the results of using a pattern recognition approach for classifying the condition of wooden railway sleepers. Railway sleeper inspections are currently done manually; visual inspection being the most common approach, with some deeper examination using an axe to judge the condition. Digital images of the sleepers were acquired to compensate for the human visual capabilities. Appropriate image analysis techniques were applied to further process the images and necessary features such as number of cracks, crack length etc have been extracted. Finally a pattern recognition and classification approach has been adopted to further classify the condition of the sleeper into classes (good or bad). A Support Vector Machine (SVM) using a Gaussian kernel has achieved good classification rate (86%) in the current case.

1 - 26 of 26
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