Recognition by the Thyroid Hormone Receptor of Canonical DNA Response Elements

To shed more light on the molecular requirements for recognition of thyroid response elements (TRES) by thyroid receptors (TRs), we compared the specific aspects of DNA TRE recognition by different TR constructs. Using fluorescence anisotropy, we performed a detailed and hierarchical study of TR-TRE binding. This wits done by comparing the binding affinities of three different TR constructs for four different TRE DNA elements, including palindromic sequences and direct repeats (F2, PAL, DR-1, and DR-4) as well as their interactions with nonspecific DNA sequences. The effect of MgCl(2) on suppressing of nonselective DNA binding to TR was also investigated. Furthermore, we determined the dissociation constants of the hTR beta DBD (DNA binding domain) and hTR beta DBD-LBD (DNA binding and ligand binding domains) for specific TRES. We found that a minimum DNA recognition peptide derived from DBD (H1TR) is sufficient for recognition and interaction with TREs, whereas scrambled DNA sequences were unrecognized. Additionally, we determined that the TR DBD binds to F2, PAL, and DR-4 with high affinity and similar K(d) values. The TR DBD-LBD recognizes all the tested TRES but binds preferentially to F2, with even higher affinity. Finally, our results demonstrate the important role played by LBDs in modulating TR-DNA binding.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.

Fabrication of disposable voltammetric electronic tongues by using Prussian Blue films electrodeposited onto CD-R gold surfaces and recognition of milk adulteration

A new approach to fabricate a disposable electronic tongue is reported. The fabrication of the disposable sensor aimed the integration of all electrodes necessary for measurement in the same device. The disposable device was constructed with gold CD-R and copper sheets substrates and the sensing elements were gold, copper and a gold surface modified with a layer of Prussian Blue. The relative standard deviation for signals obtained from 20 different disposable gold and 10 different disposable copper electrodes was below 3.5%. The performance, electrode materials and the capability of the device to differentiate samples were evaluated for taste substances model, milk with different pasteurization processes (homogenized/pasteurized, ultra high temperature (UHT) pasteurized and UHT pasteurized with low fat content) and adulterated with hydrogen peroxide. In all analysed cases, a good separation between different samples was noticed in the score plots obtained from the principal component analysis (PCA). Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

An Upgrade of Network Traffic Recognition System for SIP/VoIP Traffic Recognition

The purpose of this project is to update the tool of Network Traffic Recognition System (NTRS) which is proprietary software of Ericsson AB and Tsinghua University, and to implement the updated tool to finish SIP/VoIP traffic recognition. Basing on the original NTRS, I analyze the traffic recognition principal of NTRS, and redesign the structure and module of the tool according to characteristics of SIP/VoIP traffic, and then finally I program to achieve the upgrade. After the final test with our SIP data trace files in the updated system, a satisfactory result is derived. The result presents that our updated system holds a rate of recognition on a confident level in the SIP session recognition as well as the VoIP call recognition. In the comparison with the software of Wireshark, our updated system has a result which is extremely close to Wireshark’s output, and the working time is much less than Wireshark. In the aspect of practicability, the memory overflow problem is avoided, and the updated system can output the specific information of SIP/VoIP traffic recognition, such as SIP type, SIP state, VoIP state, etc. The upgrade fulfills the demand of this project.

Hand Gesture Detection & Recognition System

The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

Patient participation : what it is and what it is not

In general, patient participation is regarded as being informed and partaking in decision making regarding one’s care and treatment. This interpretation is common in legislation throughout the Western world and corresponding documents guiding health care professionals, as well as in scientific studies. Even though this understanding of the word participation can be traced to a growing emphasis on individuals’ autonomy in society and to certain dictionary defi nitions, there are other ways of understanding participation from a semantic point of view, and no trace of patients’ descriptions of what it is to participate can be found in these definitions. Hence, the aim of this dissertation was to understand patients’ experience of the phenomenon of patient participation. An additional aim was to understand patients’ experience of non-participation and to describe the conditions for patient participation and non-participation, in order to understand the prerequisites for patient participation. The dissertation comprises four papers. The philosophical ideas of Ricoeur provided a basis for the studies: how communication can present ways to understand and explain experiences of phenomena through phenomenological hermeneutics. The first and second studies involved a group of patients living with chronic heart failure. For the fi rst study, 10 patients were interviewed, with a narrative approach, about their experience of participation and non-participation, as defi ned by the participants. For the second study, 11 visits by three patients at a nurse-led outpatient clinic were observed, and consecutive interviews were performed with the patients and the nurses, investigating what they experience as patient participation and non-participation. A triangulation of data was performed to analyse the occurrence of the phenomena in the observed visits. For paper 3 and 4, a questionnaire was developed and distributed among a diverse group of people who had recent experience of being patients. The questionnaire comprised respondent’s description of what patient participation is, using items based on findings in Study 1, along with open-ended questions for additional aspects and general issues regarding situations in which the respondent had experienced patient participation and/or non-participation. The findings show additional aspects to patient participation: patient participation is being provided with information and knowledge in order for one to comprehend one’s body, disease, and treatment and to be able to take self-care actions based on the context and one’s values. Participation was also found to include providing the information and knowledge one has about the experience of illness and symptoms and of one’s situation. Participation occurs when being listened to and being recognised as an individual and a partner in the health care team. Non-participation, on the other hand, occurs when one is regarded as a symptom, a problem to be solved. To avoid non-participation, the information provided needs to be based on the individual’s need and with recognition of the patient’s knowledge and context. In conclusion, patient participation needs to be reconsidered in health care regulations and in clinical settings: patients’ defi nitions of participation, found to be close to the dictionaries’ description of sharing, should be recognised and opportunities provided for sharing knowledge and experience in two-way-communication.

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

Dialekter och röstigenkänning : Ett röstigenkännings-API:s förmåga att uppfatta svenska dialekters kännetecken och röstkombinationer

Allt eftersom utvecklingen går framåt inom applikationer och system så förändras också sättet på vilket vi interagerar med systemet på. Hittills har navigering och användning av applikationer och system mestadels skett med händerna och då genom mus och tangentbord. På senare tid så har navigering via touch-skärmar och rösten blivit allt mer vanligt. Då man ska styra en applikation med hjälp av rösten är det viktigt att vem som helst kan styra applikationen, oavsett vilken dialekt man har. För att kunna se hur korrekt ett röstigenkännings-API (Application Programming Interface) uppfattar svenska dialekter så initierades denna studie med dokumentstudier om dialekters kännetecken och ljudkombinationer. Dessa kännetecken och ljudkombinationer låg till grund för de ord vi valt ut till att testa API:et med. Varje dialekt fick alltså ett ord uppbyggt för att vara extra svårt för API:et att uppfatta när det uttalades av just den aktuella dialekten. Därefter utvecklades en prototyp, närmare bestämt en android-applikation som fungerade som ett verktyg i datainsamlingen. Då arbetet innehåller en prototyp och en undersökning så valdes Design and Creation Research som forskningsstrategi med datainsamlingsmetoderna dokumentstudier och observationer för att få önskat resultat. Data samlades in via observationer med prototypen som hjälpmedel och med hjälp av dokumentstudier. Det empiriska data som registrerats via observationerna och med hjälp av applikationen påvisade att vissa dialekter var lättare för API:et att uppfatta korrekt. I vissa fall var resultaten väntade då vissa ord uppbyggda av ljudkombinationer i enlighet med teorin skulle uttalas väldigt speciellt av en viss dialekt. Ibland blev det väldigt låga resultat på just dessa ord men i andra fall förvånansvärt höga. Slutsatsen vi drog av detta var att de ord vi valt ut med en baktanke om att de skulle få låga resultat för den speciella dialekten endast visade sig stämma vid två tillfällen. Det var istället det ord innehållande sje- och tje-ljud som enligt teorin var gemensamma kännetecken för alla dialekter som fick lägst resultat överlag.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Motion cue analysis for parkinsonian gait recognition

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.