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<p>An embedded measurement system for foot orthosis during gait is proposed. Strain gauge sensors were mounted on a foot orthosis to give information about strain in the sagittal plane. The ankle angle of the orthosis was fixed and strain characteristics were therefore changed when walking on slopes. With a Fourier series representation of the strain during a gait cycle, ground angle at different walking speeds and inclinations could be estimated with similar accuracy as previous studies using kinematically based estimators. Furthermore, if the angle of the mechanical foot ankle was changed, the sensing technique still could estimate ground angle without need for recalibration as opposed to kinematical sensors. This indicates that embedded strain sensors can be used for online control of future orthoses with inclination adaptation. Also, there would be no need to recalibrate the sensing system when changing shoes with different heel heights.</p> +
<p>We present an option for CCD colour camera based ink density measurements in newspaper printing. To solve the task, first, a reflectance spectrum is reconstructed from the CCD colour camera RGB values and then a well-known relation between ink density and the reflectance spectrum of a sample being measured is used to compute the density. To achieve an acceptable spectral reconstruction accuracy, the local kernel ridge regression is employed. The superiority of the local kernel ridge regression over the global regression and the popular ordinary polynomial regression is demonstrated by experimental comparisons. For a database consisting of 250 colour patches printed on newsprint by an ordinary offset printing press, the average spectrum reconstruction error of <img src="http://www.sciencedirect.com/cache/MiamiImageURL/B6V2M-4MGVJ1B-1-46/0?wchp=dGLzVzz-zSkzS" /> and the maximum error ΔE<sub>max</sub>=3.29 was obtained. Such an error proved to be low enough for achieving the average ink density measuring error lower than 0.01D.</p> +
<p>Deviation detection is important for self-monitoring systems. To perform deviation detection well requires methods that, given only "normal" data from a distribution of unknown parametric form, can produce a reliable statistic for rejecting the null hypothesis, i.e. evidence for devating data. One measure of the strength of this evidence based on the data is the p-value, but few deviation detection methods utilize p-value estimation. We compare three methods that can be used to produce p-values: one class support vector machine (OCSVM), conformal anomaly detection (CAD), and a simple "most central pattern" (MCP) algorithm. The SVM and the CAD method should be able to handle a distribution of any shape. The methods are evaluated on synthetic data sets to test and illustrate their strengths and weaknesses, and on data from a real life self-monitoring scenario with a city bus fleet in normal traffic. The OCSVM has a Gaussian kernel for the synthetic data and a Hellinger kernel for the empirical data. The MCP method uses the Mahalanobis metric for the synthetic data and the Hellinger metric for the empirical data. The CAD uses the same metrics as the MCP method and has a k-nearest neighbour (kNN) non-conformity measure for both sets. The conclusion is that all three methods give reasonable, and quite similar, results on the real life data set but that they have clear strengths and weaknesses on the synthetic data sets. The MCP algorithm is quick and accurate when the "normal" data distribution is unimodal and symmetric (with the chosen metric) but not otherwise. The OCSVM is a bit cumbersome to use to create (quantized) p-values but is accurate and reliable when the data distribution is multimodal and asymmetric. The CAD is also accurate for multimodal and asymmetric distributions. The experiment on the vehicle data illustrate how algorithms like these can be used in a self-monitoring system that uses a fleet of vehicles to conduct deviation detection without supervisi- n and without prior knowledge about what is being monitored.</p>
Estimating pressure peak position and air-fuel ratio using the ionization current and artificial neural networks +
<p>We propose two artificial neural network models which use the ionization current for estimation of the position of the pressure peak and the air-fuel ratio. The pressure peak position model produces estimates on a cycle-by-cycle basis for each of the cylinders. These estimates are twice as good as estimates obtained from a linear model. The air-fuel ratio model uses the universal exhaust gas oxygen sensor as reference; it produces estimates that are ten times better than estimates obtained fi om a linear model.</p> +
Estimating pressure peak position and air-fuel ratio using the ionization current and artificial neural networks +
Wickstrom, N., Taveniku, M., Linde, A., Larsson, M., Svensson, B. +
<p>This paper is concerned with the offset lithographic colour printing. To obtain high quality colour prints, given proportions of cyan (C), magenta (M), yellow (Y), and black (K) inks (four primary inks used in the printing process) should be accurately maintained in any area of the printed picture. To accomplish the task, the press operator needs to measure the printed result for assessing the proportions and use the measurement results to reduce the colour deviations. Specially designed colour bars are usually printed to enable the measurements. This paper presents an approach to estimate the proportions directly in colour pictures without using any dedicated areas. The proportions—the average amount of C, M, Y, and K inks in the area of interest—are estimated from the CCD colour camera RGB (L*a*b*) values recorded from that area. The local kernel ridge regression and the support vector regression are combined for obtaining the desired mapping L*a*b* ⇒ CMYK, which can be multi-valued.</p> +
<p>When water is removed from the paper during paper making, a dimensional change occurs in which the paper shrinks in the direction perpendicular to the direction of processing. The dimensional changes vary across the web and influence, e.g., the surface and compression properties of the paper; they also complicate the control of the paper machine. In this article, a robust method for estimating the relative shrinkage profile is presented. The method is based on a one-dimensional recording of the imprints from the forming fabric, using a fluorescence technique. The recording is transformed into a time-frequency spectrum, on which three different frequency estimators have been evaluated. In simulations on synthetic data and measurements on paper profiles the estimator that maximizes the correlation energy showed the most robust and accurate performance of the methods evaluated, even at a low signal-to-noise ratio.</p> +
<p>This paper investigates the use of the ionization current to estimate the Coefficient of Variation for the Indicated Mean Effective Pressure, COV(IMEP), which is a common variable for combustion stability in a spark-ignited engine. Stable combustion in this definition implies that the variance of the produced work, measured over a number of consecutive combustion cycles, is small compared to the mean of the produced work. The COV(IMEP) is varied experimentally either by increasing EGR flow or by changing the air-fuel ratio, in both a laboratory setting (engine in dynamometer) and in an on-road setting. The experiments show a positive correlation between COV(Ion integral), the Coefficient of Variation for the integrated Ion Current, and COV(IMEP), when measured under low load on an engine in a dynamometer, but not under high load conditions. On-road experiments show a positive correlation, but only in the EGR and the lean burn case. An approach based on individual cycle classification for real-time estimation of combustion stability is discussed.</p> +
<p>This paper investigates the use of the ionization current to estimate the Coefficient of Variation for the Indicated Mean Effective Pressure, COV(IMEP), which is a common variable for combustion stability in a spark-ignited engine. Stable combustion in this definition implies that the variance of the produced work, measured over a number of consecutive combustion cycles, is small compared to the mean of the produced work. The COV(IMEP) is varied experimentally either by increasing EGR flow or by changing the air-fuel ratio, in both a laboratory setting (engine in dynamometer) and in an on-road setting. The experiments show a positive correlation between COV(Ion integral), the Coefficient of Variation for the integrated Ion Current, and COV(IMEP), when measured under low load on an engine in a dynamometer, but not under high load conditions. On-road experiments show a positive correlation, but only in the EGR and the lean burn case. An approach based on individual cycle classification for real-time estimation of combustion stability is discussed. © Copyright 2001 Society of Automotive Engineers, Inc.</p> +
Estimation of the Trunk Attitude of a Humanoid by Data Fusion of Inertial Sensors and Joint Encoders +
<p>The major problem associated with the walking of humanoid robots is to main- tain its dynamic equilibrium while walking. To achieve this one must detect gait instability during walking to apply proper fall avoidance schemes and bring back the robot into stable equilibrium. A good approach to detect gait insta- bility is to study the evolution of the attitude of the humanoid's trunk. Most attitude estimation techniques involve using the information from inertial sen- sors positioned at the trunk. However, inertial sensors like accelerometer and gyro are highly prone to noise which lead to poor attitude estimates that can cause false fall detections and falsely trigger fall avoidance schemes. In this paper we present a novel way to access the information from joint encoders present in the legs and fuse it with the information from inertial sensors to provide a highly improved attitude estimate during humanoid walk. Also if the joint encoders' attitude measure is compared separately with the IMU's atti- tude estimate, then it is observed that they are different when there is a change of contact between the stance leg and the ground. This may be used to detect a loss of contact and can be verified by the information from force sensors present at the feet of the robot. The propositions are validated by experiments performed on humanoid robot NAO. Copyright © 2013 by World Scientific Publishing Co. Pte. Ltd.</p> +
<p>Many languages in Ethiopia use a unique alphabet called Ethiopic for writing. However, there is no OCR system developed to date. In an effort to develop automatic recognition of Ethiopic script, a novel system is designed by applying structural and syntactic techniques. The recognition system is developed by extracting primitive structural features and their spatial relationships. A special tree structure is used to represent the spatial relationship of primitive structures. For each character, a unique string pattern is generated from the tree and recognition is achieved by matching the string against a stored knowledge base of the alphabet. To implement the recognition system, we use direction field tensor as a tool for character segmentation, and extraction of structural features and their spatial relationships. Experimental results are reported.</p> +
<p>In this paper we describe the acquisition and content of a large database of Ethiopic documents for testing and evaluating character recognition systems. The Ethiopic Document Image Database (EDIDB) contains documents written in Amharic and Geez languages. The database was built from a variety of documents such as printouts, books, newspapers, and magazines. Documents written in various font types, sizes and styles were included in the database. Degraded and poor quality documents were also included in the database to represent the real life situation. A total of 1,204 pages were scanned at a resolution of 300 dpi and saved as grayscale images of JPEG format. We also describe an evaluation protocol for standardizing the comparison of recognition systems and their results. The database is made available to the research community through http://www.hh.se/staff/josef/.</p> +
<p>A technique evaluating liveness in short face image sequences is presented The intended purpose of the proposed system is to assist in a biometric authentication framework, by adding liveness awareness in a non-intrusive manner. Analyzing the trajectories of single parts of a live face reveal valuable information to discriminate it against a spoofed one. The proposed system uses a lightweight novel optical flow, which is especially applicable in face motion estimation based on the structure tensor and a few frames. It uses a model-based local Gabor decomposition and SVM experts for face part detection. An alternative approach for face pan detection using optical flow pattern matching is introduced as well. Experimental results on the proposed system are presented.</p> +
Evaluation Protocol for the DXM2VTS Database and Performance Comparison of Face Detection and Face Tracking on Video +
<p>Video databases and their corresponding evaluation protocols are used to compare classifiers, such as face detection an tracking. In this paper, a six level evaluation protocol for the Damascened XM2VTS (DXM2VTS) database is presented to measure face detection and tracking performance. Additionally, a novel database containing thousands of videos is created by combining video from the XM2VTS database with a set of newly recorded standardized real-life video used as background and with several realistic degradations, such as motion blur, noise, etc. Moreover, two publicly available and published face detection algorithms, are tested on the six suggested difficulty levels of the protocol. Their performance on video in terms of false acceptance, false rejection, correct detection, and repeatability, are reported and conclusions are drawn.</p> +
Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal +
<p>Non-linear acoustic technique is an attractive approach in evaluating early fatigue as well as cracks in material. However, its accuracy is greatly restricted by external non-linearities of ultra-sonic measurement systems. In this work, an acoustical data-driven deviation detection method, called the consensus self-organizing models (COSMO) based on statistical probability models, was introduced to study the evolution of localized crack growth. By using pitch-catch technique, frequency spectra of acoustic echoes collected from different locations of a specimen were compared, resulting in a Hellinger distance matrix to construct statistical parameters such as z-score, p-value and T-value. It is shown that statistical significance p-value of COSMO method has a strong relationship with the crack growth. Particularly, T-values, logarithm transformed p-value, increases proportionally with the growth of cracks, which thus can be applied to locate the position of cracks and monitor the deterioration of materials. © 2018 by the authors. </p> +
Evaluation of Extensibility, Portability and Scalability in a Database-centric System Architecture for Smart Home Environments +
<p>Advances in database technology allow modern database systems to serve as a platform for the development, deployment and management of smart home environments and ambient assisted living systems. This work investigates non-functional issues of a database-centric system architecture for smart home environments when: (i) extending the system with new functionalities other than data storage, such as on-line reactive behaviors and advanced processing of longitudinal information, (ii) porting the whole system to different operating systems on distinct hardware platforms, and (iii) scaling the system by incrementally adding new instances of a given functionality. The outcome of the evaluation is demonstrated, and analyzed, for three test functionalities on three heterogeneous computing platforms. As a contribution, this work can help developers in identifying which architectural components in the database-centric system architecture that may become performance bottlenecks when extending, porting and scaling the system.</p> +
<p>The European V-Charge project seeks to develop fully automated valet parking and charging of electric vehicles using only low-cost sensors. One of the challenges is to implement robust visual localization using only cameras and stock vehicle sensors. We integrated four monocular, wide-angle, fisheye cameras on a consumer car and implemented a mapping and localization pipeline. Visual features and odometry are combined to build and localize against a keyframe-based three dimensional map. We report results for the first stage of the project, based on two months worth of data acquired under varying conditions, with the objective of localizing against a map created offline. © 2013 IEEE.</p> +
<p>Evaluating the health condition of a material that could potentially contain micro-flaws is a common and important application within the field of non-destructive testing. Examples of such micro-defects include dislocation, fatigue cracks or impurities and are often hard to detect. The ability to precisely measure their type, size and position is a prerequisite for estimating the remaining useful life of the component. One technique that was shown successful in the past is based on traditional ultrasonic testing methods. In most cases, inner micro-flaws induce slight changes of acoustic wave spectrum components. However, these changes are often difficult to detect directly, as they tend to exhibit features that are most naturally analyzed using statistical and probabilistic methods. In this paper we apply Consensus Self-Organizing Models (COSMO) method to detect micro-flaws in metallic material. This approach is essentially an unsupervised deviation detection method based on the concept of "wisdom of the crowd". This method is used to analyze the spectrum of acoustic waves received by the transducer attached on the surface of material being analyzed. We have modeled a steel board with micro-cracks and collected time-series of acoustic echo response, at different positions on material's surface. The experimental results show that the COSMO method is able to detect and locate micro-flaws. © 2016 IEEE</p> +
<p>Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to today's vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper we evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden. © The Authors. Published by Elsevier B.V.</p> +
Evaluation of a new probabilistic approach to scoring algorithms in protein identification by Peptide Mass Fingerprinting (PMF) +
<p>The protein identification performs a crucial role in the contemporary medicine. Proteins may act as the potential biomarkers for investigating many diseases, e.g. the civilization-related ones. Peptide mass fingerprinting (PMF) is a widely used protein identification method basing on mass spectrometry data. Economical reasons and time savings are of great importance in the identification experiments. Thereby, innovative ideas, which have the potential to improve the PMF identification, are still desired. A novel probability-based scoring scheme, which constitutes the last part of the PMF identification procedure, was developed. Presented scoring scheme incorporates an innovative idea, which assumes a different approach to modelling the distribution of proteins derived from the database, on the basis of which the score is computed. In the paper we assess a performance of the proposed scoring method against popular scoring scheme, i.e. Mascot (http://www.matrixscience.com/). The comparison of the methods includes scoring results obtained for the simulated data. Different levels of proteins samples contamination and different coverage of peptides sequences were considered in the empirical study.</p> +
