1,091 research outputs found

    A structured light solution for detecting scapular dyskinesis

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    Scapular dyskinesis is a common occurrence in overhead athletes, i.e. athletes who participate in any sport where the upper arm and shoulder is used above the athlete’s head. However, no consensus has been reached on how to evaluate scapular dyskinesis quantitatively. In this thesis, we developed a measuring tool that can be used to evaluate certain key clinical parameters specific to scapular dyskinesis. The tool employs a 3D structured light computer vision approach to create a surface map of the soft-tissue across the scapula. This surface map is then analysed using surface curvature analysis techniques to identify the key clinical parameters associated with scapular dyskinesis. The main advantage of this method is that it provides a measurement tool that may facilitate future quantitative analysis of these key parameters. This may aid with diagnosis and monitoring of the condition by allowing measurement data to be collected both before and after treatment and rehabilitation. We expect that this tool will make the monitoring of treatment effectiveness easier while contributing to diagnostic computer vision

    Reverse engineering of printed circuit boards: A conceptual idea

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    One of the backbones in electronic manufacturing industry is the printed circuit board (PCB).The recent rapid growth in electronics devices, results escalating in the production number of the PCBs.For electronic equipment and appliances which are PCB based, new generations of PCB's are produced to suit the requirements of new products.This development can lead to waste and inefficiency when perfectly serviceable electronic components and appliances have to be scrapped because of the unavailability of spare PCB's from the Original Equipment Manufacturer (OEM) or are already obsolete.This paper proposed a novel framework for reverse engineering of obsolete single layer PCB.Equivalent PCB's which can be used as spares will be reproduced utilizing this new framework.This framework involves several steps, such as Data Acquisition, Image Processing, CAD Editing, PCB Fabrication and Circuitry testing and Analysis.Each stage of the framework and the functionality evaluation of the reproduced PCB will be discussed in detail in following sections

    Differences in the Central Neural Activation under Emotional Stress across the Menstrual Cycle

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    The present study had several goals. First, we aimed to investigate the potential differences in the activation of the corticolimbic structures during emotional stress in healthy women across the menstrual cycle using stress imagery. Second, we searched for differences in the subjective anxiety under emotional stress across the menstrual cycle and tried to correlate the perceived level of anxiety to activation of the specific corticolimbic structures. Third, we attempted to compare central neural activation of women in follicular and in luteal phases of the menstrual cycle separately to that of men during emotional stress to investigate potential differences in neural response. We used perfusion based functional magnetic resonance imaging (MRI) and blood oxygen level dependent (BOLD) contrast to measure cerebral blood flow response to the emotional stress using stress imagery in 29 healthy volunteers (9 women in follicular phase, 10 women in luteal phase, and 10 men). Cycle-dependent comparison of the stress response in women revealed that women in the follicular phase had greater activation in the areas of the ventro-medial prefrontal cortex (VMPFC), with levels of activation comparable to those of men, and anterior insula, while women in the luteal phase of their menstrual cycle demonstrated increase blood flow in the areas of the anterior cingulate and hippocampus at P = 0.01. Males showed overall greater degree of corticolimbic activation, specifically in the bilateral hippocampi and right prefrontal cortex regardless of which group of women they were compared to. When compared to women in different phases of the menstrual cycle specifically, men showed greater cerebral blood flow in bilateral cingulate cortices and right hippocampus compared to women in the follicular phase, and bilateral striatum, amygdala, bilateral hippocampi when compared to women in the luteal phase. We did not observe different levels of self-reported anxiety during stress imagery across the menstrual cycle, however, women in their luteal phase showed a positive correlation of the self-reported anxiety levels and cerebral blood flow in the posterior insula at the threshold level of P = 0.05. The results of our study are consistent with the previously available information regarding the differences in the corticolimbic activation across the menstrual cycle in women and in women vs. men. In addition to that, our data supports the correlation of the levels of anxiety and insular activation in the luteal phase of the menstrual cycle and could represent an initial step in uncovering the mechanisms regulating stress response, anxiety and their relation to the hormonal status

    Functional Organization of the Human Brain: How We See, Feel, and Decide.

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    The human brain is responsible for constructing how we perceive, think, and act in the world around us. The organization of these functions is intricately distributed throughout the brain. Here, I discuss how functional magnetic resonance imaging (fMRI) was employed to understand three broad questions: how do we see, feel, and decide? First, high-resolution fMRI was used to measure the polar angle representation of saccadic eye movements in the superior colliculus. We found that eye movements along the superior-inferior visual field are mapped across the medial-lateral anatomy of a subcortical midbrain structure, the superior colliculus (SC). This result is consistent with the topography in monkey SC. Second, we measured the empathic responses of the brain as people watched a hand get painfully stabbed with a needle. We found that if the hand was labeled as belonging to the same religion as the observer, the empathic neural response was heightened, creating a strong ingroup bias that could not be readily manipulated. Third, we measured brain activity in individuals as they made free decisions (i.e., choosing randomly which of two buttons to press) and found the activity within fronto-thalamic networks to be significantly decreased compared to being instructed (forced) to press a particular button. I also summarize findings from several other projects ranging from addiction therapies to decoding visual imagination to how corporations are represented as people. Together, these approaches illustrate how functional neuroimaging can be used to understand the organization of the human brain

    High-resolution fMRI of content-sensitive subsequent memory responses in human medial temporal lobe

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    Abstract & The essential role of the medial temporal lobe (MTL) in long-term memory for individual events is well established, yet important questions remain regarding the mnemonic functions of the component structures that constitute the region. Within the hippocampus, recent functional neuroimaging findings suggest that formation of new memories depends on the dentate gyrus and the CA 3 field, whereas the contribution of the subiculum may be limited to retrieval. During encoding, it has been further hypothesized that structures within MTL cortex contribute to encoding in a content-sensitive manner, whereas hippocampal structures may contribute to encoding in a more domain-general manner. In the current experiment, highresolution fMRI techniques were utilized to assess novelty and subsequent memory effects in MTL subregions for two classes of stimuli-faces and scenes. During scanning, participants performed an incidental encoding (target detection) task with novel and repeated faces and scenes. Subsequent recognition memory was indexed for the novel stimuli encountered during scanning. Analyses revealed voxels sensitive to both novel faces and novel scenes in all MTL regions. However, similar percentages of voxels were sensitive to novel faces and scenes in perirhinal cortex, entorhinal cortex, and a combined region comprising the dentate gyrus, CA 2 , and CA 3 , whereas parahippocampal cortex, CA 1 , and subiculum demonstrated greater sensitivity to novel scene stimuli. Paralleling these findings, subsequent memory effects in perirhinal cortex were observed for both faces and scenes, with the magnitude of encoding activation being related to later memory strength, as indexed by a graded response tracking recognition confidence, whereas subsequent memory effects were scene-selective in parahippocampal cortex. Within the hippocampus, encoding activation in the subiculum correlated with subsequent memory for both stimulus classes, with the magnitude of encoding activation varying in a graded manner with later memory strength. Collectively, these findings suggest a gradient of content sensitivity from posterior (parahippocampal) to anterior (perirhinal) MTL cortex, with MTL cortical regions differentially contributing to successful encoding based on event content. In contrast to recent suggestions, the present data further indicate that the subiculum may contribute to successful encoding irrespective of event content. &amp

    Automatic handwriter identification using advanced machine learning

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    Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

    Digital Library Services for Three-Dimensional Models

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    With the growth in computing, storage and networking infrastructure, it is becoming increasingly feasible for multimedia professionals—such as graphic designers in commercial, manufacturing, scientific and entertainment areas—to work with 3D digital models of the objects with which they deal in their domain. Unfortunately most of these models exist in individual repositories, and are not accessible to geographically distributed professionals who are in need of them. Building an efficient digital library system presents a number of challenges. In particular, the following issues need to be addressed: (1) What is the best way of representing 3D models in a digital library, so that the searches can be done faster? (2) How to compress and deliver the 3D models to reduce the storage and bandwidth requirements? (3) How can we represent the user\u27s view on similarity between two objects? (4) What search types can be used to enhance the usability of the digital library and how can we implement these searches, what are the trade-offs? In this research, we have developed a digital library architecture for 3D models that addresses the above issues as well as other technical issues. We have developed a prototype for our 3D digital library (3DLIB) that supports compressed storage, along with retrieval of 3D models. The prototype also supports search and discovery services that are targeted for 3-D models. The key to 3DLIB is a representation of a 3D model that is based on “surface signatures”. This representation captures the shape information of any free-form surface and encodes it into a set of 2D images. We have developed a shape similarity search technique that uses the signature images to compare 3D models. One advantage of the proposed technique is that it works in the compressed domain, thus it eliminates the need for uncompressing in content-based search. Moreover, we have developed an efficient discovery service consisting of a multi-level hierarchical browsing service that enables users to navigate large sets of 3D models. To implement this targeted browsing (find an object that is similar to a given object in a large collection through browsing) we abstract a large set of 3D models to a small set of representative models (key models). The abstraction is based on shape similarity and uses specially tailored clustering techniques. The browsing service applies clustering recursively to limit the number of key models that a user views at any time. We have evaluated the performance of our digital library services using the Princeton Shape Benchmark (PSB) and it shows significantly better precision and recall, as compared to other approaches

    Imaging displacement and strain in the medial gastrocnemius muscle during ankle-joint motion using 2D-ciné DENSE MRI

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    Skeletal muscle structure has been defined on both macro and microscopic levels by gross dissection, light- and electron-microscopy. The basic physiological building blocks involve the electromechanical coupling between interlinking actin and myosin fibres. Detailed intramuscular behaviour during contraction can be clearly defined when examining a single isolated muscle. However, there are few areas in the human body where single muscles act independently to affect motion. This thesis attempts to address the compounded effect that muscles have on each other, while working synergistically in a group, such as the calf muscle

    Cartographic modelling for automated map generation

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    Information Preserving Processing of Noisy Handwritten Document Images

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    Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%
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