Property:Abstract
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<p>Active contour model (ACM) is an image segmentation technique widely applied for object detection. Most of the research in ACM area is dedicated to the development of various energy functions based on physical intuition. Here, instead of constructing a new energy function, we manipulate values of ACM parameters to generate a multitude of potential contours, score them using a machine-learned ranking technique, and select the best contour for each object in question. Several learning-to-rank (L2R) methods are evaluated with a goal to choose the most accurate in assessing the quality of generated contours. Superiority of the proposed segmentation approach over the original boosted edge-based ACM and three ACM implementations using the level-set framework is demonstrated for the task of Prorocentrum minimum cells’ detection in phytoplankton images. Experiments show that diverse set of contour features with grading learned by a variant of multiple additive regression trees (λ-MART) helped to extract precise contour for 87.6 % of cells tested.</p> +
<p>A pneumatic loudspeaker for intensive sounds is presented. It operates by modulation of air from over- and underpressure reservoirs, as opposed to siren-like loudspeakers that use compressed air only. The symmetric construction makes the behavior more linear both with respect to aperture amplitudes and frequency bandwidth. Therefore, it may be used as secondary source in industrial active noise control problems where generation of large volume velocities are necessary. It is also shown that a Hammerstein model can model the loudspeaker over a wide frequency band. © 2001 EUCA.</p> +
<p>A pole-projection approach is proposed as a useful tool for multi-objective robust control design. Different load conditions or nonlinearities are considered in the design by simultaneously stabilizing a set of linear models. The idea is to repeatedly project the poles for each model (one at a time) to a generalized stability region until all models are stabilized. Similarly, pole projections are also performed for an auxiliary set of models. Stability of the latter gives guaranteed bounds on different sensitivity functions for the former. The method solves a benchmark problem for which a controller of lower complexity than has been reported before is obtained. © 1999 EUCA.</p> +
<p>With the increased degree of miniaturization resulting from the use of modem VLSI technology and the high communication bandwidth available through optical connections, it is now possible to build massively parallel computers based on distributed modules which can be embedded in advanced industrial products. Examples of such future possibilities are ''action-oriented systems'', in which a network of highly parallel modules perform a multitude of tasks related to perception, cognition, and action. The paper discusses questions of architecture on the level of modules and inter-module communication and gives concrete architectural solutions which meet the demands of typical, advanced industrial real-time applications. The interface between the processors arrays and the all-optical communication network is described in some detail. Implementation issues specifically related to the demand for miniaturization are discussed.</p> +
<p>In this paper, a novel approach for printed character recognition using linear symmetry is proposed. When the conventional character recognition methods such as the artificial neural network based techniques are used to recognise Brahmi Sinhala script, segmentation of modified characters into modifier symbols and basic characters is a necessity but a complex issue. The large size of the character set makes the whole recognition process even more complex. In contrast, in the proposed method, the orientation features are effectively used to recognise characters directly using a standard alphabet as the basis without the need for segmentation into basic components. The edge detection algorithm using linear symmetry recognises vertical modifiers. The linear symmetry principle is also used to determine the skew angle. Experiments with the aim for an optical character recognition system for the printed Sinhala script show favourable results.</p> +
<p>An approach is proposed for automatic fault detection in a population of mechatronic systems. The idea is to employ self-organizing algorithms that produce low-dimensional representations of sensor and actuator values on the vehicles, and compare these low-dimensional representations among the systems. If a representation in one vehicle is found to deviate from, or to be not so similar to, the representations for the majority of the vehicles, then the vehicle is labeled for diagnostics. The presented approach makes use of principal component coding and a measure of distance between linear sub-spaces. The method is successfully demonstrated using simulated data for a commercial vehiclepsilas engine coolant system, and using real data for computer hard drives.</p> +
<p>We present a simple trick to get an approximate estimate of the weight decay parameter lambda. The method combines early stopping and weight decay, into the estimate lambda=parallel to del E(W(es))parallel to/parallel to 2W(es)parallel to, where W(es) is the set of weights at the early stopping point, and E(W) is the training data fit error. The estimate is demonstrated and compared to the standard cross-validation procedure for lambda selection on one synthetic and four real life data sets. The result is that lambda is as good an estimator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute. The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stop ping.</p> +
<p>Periocular refers to the facial region in the vicinity of the eye, including eyelids, lashes and eyebrows. While face and irises have been extensively studied, the periocular region has emerged as a promising trait for unconstrained biometrics, following demands for increased robustness of face or iris systems. With a surprisingly high discrimination ability, this region can be easily obtained with existing setups for face and iris, and the requirement of user cooperation can be relaxed, thus facilitating the interaction with biometric systems. It is also available over a wide range of distances even when the iris texture cannot be reliably obtained (low resolution) or under partial face occlusion (close distances). Here, we review the state of the art in periocular biometrics research. A number of aspects are described, including: (i) existing databases, (ii) algorithms for periocular detection and/or segmentation, (iii) features employed for recognition, (iv) identification of the most discriminative regions of the periocular area, (v) comparison with iris and face modalities, (vi) soft-biometrics (gender/ethnicity classification), and (vii) impact of gender transformation and plastic surgery on the recognition accuracy. This work is expected to provide an insight of the most relevant issues in periocular biometrics, giving a comprehensive coverage of the existing literature and current state of the art. © 2015 Elsevier B.V. All rights reserved.</p> +
<p>In the future, mobile robots will most probably navigate through the fields autonomously to perform different kind of agricultural operations. As most crops are cultivated in rows, an important step towards this long-term goal is the development of a row-recognition system, which will allow a robot to accurately follow a row of plants. In this paper we describe a new method for robust recognition of plant rows based on the Hough transform. Our method adapts to the size of plants, is able to fuse information coming from two rows or more and is very robust against the presence of many weeds. The accuracy of the position estimation relative to the row proved to be good with a standard deviation between 0.6 and 1.2 cm depending on the plant size. The system has been tested on both an inter-row cultivator and a mobile robot. Extensive field tests have showed that the system is sufficiently accurate and fast to control the cultivator and the mobile robot in a closed-loop fashion with a standard deviation of the position of 2.7 and 2.3 cm, respectively. The vision system is also able to detect exceptional situations by itself, for example the occurrence of the end of a row.</p> +
<p>In this paper we have developed a mobile robot which is able to perform crop-scale operations using vision as only sensor. The system consists of a row-following system and a visual odometry system. The row following system captures images from a front looking camera on the robot and the crop rows are extracted using Hough transform. Both distance to the rows and heading angle is provided which both are used to control the steering. The visual odometry system uses two cameras in a stereo setup pointing perpendicular to the ground. This system measures the travelled distance by measuring the ground movement and compensate for height variation. Experiments are performed on an artificial field due to the season. The result shows that the visual odometry have accuracy 1.3% of travelled distance.</p> +
A wearable gait analysis system using inertial sensors Part I : Evaluation of measures of gait symmetry and normality against 3D kinematic data +
<p>Gait analysis (GA) is an important tool in the assessment of several physical and cognitive conditions. The lack of simple and economically viable quantitative GA systems has hindered the routine clinical use of GA in many areas. As a result, patients may be receiving sub-optimal treatment. The present study introduces and evaluates measures of gait symmetry and gait normality calculated from inertial sensor data. These indices support the creation of mobile, cheap and easy to use quantitative GA systems. The proposed method was compared to measures of symmetry and normality derived from 3D kinematic data. Results show that the proposed method is well correlated to the kinematic analysis in both symmetry (r=0.84, p<0.0001) and normality (r=0.81, p<0.0001). In addition, the proposed indices can be used to classify normal from abnormal gait.</p> +
A wearable gait analysis system using inertial sensors Part I: evaluation of measures of gait symmetry and normality against 3D kinematic data +
<p>Gait analysis (GA) is an important tool in the assessment of several physical and cognitive conditions. The lack of simple and economically viable quantitative GA systems has hindered the routine clinical use of GA in many areas. As a result, patients may be receiving sub-optimal treatment. The present study introduces and evaluates measures of gait symmetry and gait normality calculated from inertial sensor data. These indices support the creation of mobile, cheap and easy to use quantitative GA systems. The proposed method was compared to measures of symmetry and normality derived from 3D kinematic data. Results show that the proposed method is well correlated to the kinematic analysis in both symmetry (r=0.84, p<0.0001) and normality (r=0.81, p<0.0001). In addition, the proposed indices can be used to classify normal from abnormal gait.</p> +
<p>The gold standard for gait analysis, in-lab 3D motion capture, is not routinely used for clinical assessment due to limitations in availability, cost and required training. Inexpensive alternatives to quantitative gait analysis are needed to increase the its adoption. Inertial sensors such as accelerometers and gyroscopes are promising tools for the development of wearable gait analysis (WGA) systems. The present study evaluates the use of a WGA system on hip-arthroplasty patients in a real clinical setting. The system provides information about gait symmetry and normality. Results show that the normality measurements are well correlated with various quantitative and qualitative measures of recovery and health status.</p> +
<p>The gold standard for gait analysis, in-lab 3D motion capture, is not routinely used for clinical assessment due to limitations in availability, cost and required training. Inexpensive alternatives to quantitative gait analysis are needed to increase the its adoption. Inertial sensors such as accelerometers and gyroscopes are promising tools for the development of wearable gait analysis (WGA) systems. The present study evaluates the use of a WGA system on hip-arthroplasty patients in a real clinical setting. The system provides information about gait symmetry and normality. Results show that the normality measurements are well correlated with various quantitative and qualitative measures of recovery and health status.</p> +
<p>This paper is concerned with the problem of image analysis based detection of local defects embedded in pavement tiles surfaces. The technique developed is based on the ICA sparse code shrinkage denoising, the local 2D discrete Walsh transform and ANN. To reduce random noise, the ICA sparse code shrinkage de-noising is applied. Next, robust local features characterizing the surface texture are extracted based on the 2D Walsh transform and then analyzed by an artificial Neural Network. A 100% correct classification rate was obtained when testing the technique proposed on a set of surface images recorded from 400 tiles.</p> +
<p>Understanding and quantifying drivers’ influenceon fuel consumption is an important and challenging problem.A number of commonly used approaches are based on collectionofAccelerator Pedal Position - Engine Speed(APPES) maps. Upuntil now, however, most publicly available results are basedon limited amounts of data collected in experiments performedunder well-controlled conditions. Before APPES maps can beconsidered a reliable solution, there is a need to evaluate theusefulness of those models on a larger and more representativedata.In this paper we present analysis of APPES maps that werecollected, under actual operating conditions, on more than1200 trips performed by a fleet of 5 Volvo trucks owned bya commercial transporter in Europe. We use Gaussian MixtureModels to identify areas of those maps that correspond todifferent types of driver behaviour, and investigate how theparameters of those models relate to variables of interest suchas vehicle weight or fuel consumption.</p> +
ARC13 -- Assessment of Research and Coproduction : Reports from the assessment of all research at Halmstad University 2013 +
<p>During 2013, an evaluation of all the research conducted at Halmstad University was carried out. The purpose was to assess the quality of the research, coproduction, and collaboration in research, as well as the impact of the research. The evaluation was dubbed the Assessment of Research and Coproduction 2013, or ARC13. (Extract from Executive Summary)</p> +
ARC13 : Assessment of Research and Coproduction : Reports from the assessment of all research at Halmstad University 2013 +
<p>During 2013, an evaluation of all the research conducted at Halmstad University was carried out. The purpose was to assess the quality of the research, coproduction, and collaboration in research, as well as the impact of the research. The evaluation was dubbed the Assessment of Research and Coproduction 2013, or ARC13. (Extract from Executive Summary)</p> +
<p>As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare.</p> +
<p>The Person-Centered Care (PCC) paradigm advocates that instead of being the passive target of a medical intervention, patients should play an active part in their care and in the decision-making process, together with clinicians. Although new mobile and wearable technologies have created a new wave of personalized health-related applications, it is still unclear how these technologies can be used in health care institutions in order to support person-centered care. In order to investigate this matter, we undertook a pilot study aimed at determining if and how activity monitoring can support person-centered care routines for patients undergoing total hip replacement surgery. This is a preliminary report describing the methodology, preliminary results, and some practical challenges. We present here an orientation-invariant, accelerometer-based activity monitoring method, especially designed to address the requirements of monitoring in-patients in a real clinical setting. We also present and discuss some practical issues related to complying with hospital requirements and collaborating with hospital staff.</p> +
