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  • 101.
    Hernandez-Diaz, Kevin
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Periocular Recognition Using CNN Features Off-the-Shelf2018In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), Piscataway, N.J.: IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these offthe-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to ~40%, with the fusion of CNN and traditional features providing additional improvements.

  • 102.
    Hofbauer, Heinz
    et al.
    University of Salzburg, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Uhl, Andreas
    University of Salzburg, Salzburg, Austria.
    Experimental Analysis Regarding the Influence of Iris Segmentation on the Recognition Rate2016In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 5, no 3, p. 200-211Article in journal (Refereed)
    Abstract [en]

    In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance. © The Institution of Engineering and Technology 2016

  • 103.
    Hofbauer, Heinz
    et al.
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wild, Peter
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Uhl, Andreas
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    A Ground Truth for Iris Segmentation2014In: 2014 22nd International Conference on Pattern Recognition (ICPR) / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2014, p. 527-532Conference paper (Refereed)
    Abstract [en]

    Classical iris biometric systems assume ideal environmental conditions and cooperative users for image acquisition. When conditions are less ideal or users are uncooperative or unaware of their biometrics being taken the image acquisition quality suffers. This makes it harder for iris localization and segmentation algorithms to properly segment the acquired image into iris and non-iris parts. Segmentation is a critical part in iris recognition systems, since errors in this initial stage are propagated to subsequent processing stages. Therefore, the performance of iris segmentation algorithms is paramount to the performance of the overall system. In order to properly evaluate and develop iris segmentation algorithm, especially under difficult conditions like off angle and significant occlusions or bad lighting, it is beneficial to directly assess the segmentation algorithm. Currently, when evaluating the performance of iris segmentation algorithms this is mostly done by utilizing the recognition rate, and consequently the overall performance of the biometric system. In order to streamline the development and assessment of iris segmentation algorithms with the dependence on the whole biometric system we have generated a iris segmentation ground truth database. We will show a method for evaluating iris segmentation performance base on this ground truth database and give examples of how to identify problematic cases in order to further analyse the segmentation algorithms. ©2014 IEEE.

  • 104.
    Johansson, Jonathan
    et al.
    Halmstad University, School of Information Technology.
    Wikdahl, Daniel
    Halmstad University, School of Information Technology.
    Human identification with radar2016Independent thesis Basic level (university diploma), 180 HE creditsStudent thesis
  • 105.
    Johnsson, Dennis
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Bengtsson, Jerker
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Svensson, Bertil
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Two-level Reconfigurable Architecture for High-Performance Signal Processing2004In: ERSA'04, The 2004 International Conference on Engineering of Reconfigurable Systems and Algorithms: The 2004 International MultiConference in Computer Science and Computer Engineering / [ed] Toomas P. Plaks, Arthens: CSREA Press, 2004, p. 177-183Conference paper (Refereed)
    Abstract [en]

    High speed signal processing is often performed as a pipeline of functions on streams or blocks of data. In order to obtain both flexibility and performance, parallel, reconfigurable array structures are suitable for such processing. The array topology can be used both on the micro and macro-levels, i.e. both when mapping a function on a fine-grained array structure and when mapping a set of functions on different nodes in a coarse-grained array. We outline an architecture on the macro-level as well as explore the use of an existing, commercial, word level reconfigurable architecture on the micro-level. We implement an FFT algorithm in order to determine how much of the available resources are needed for controlling the computations. Having no program memory and instruction sequencing available, a large fraction, 70%, of the used resources is used for controlling the computations, but this is still more efficient than having statically dedicated resources for control. Data can stream through the array at maximum I/O rate, while computing FFTs. The paper also shows how pipelining of the FFT algorithm over a two-level reconfigurable array of arrays can be done in various ways, depending on the application demands.

  • 106.
    Juneby, Hans
    et al.
    Halmstad University, School of Information Technology.
    Can, Mikael
    Halmstad University, School of Information Technology.
    Newtons andra vagn2015Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    A problem of basic physics education in schools is that gravitation constantly interferes with experiments and demonstrations, making it difficult for students to understand Newton's first and second laws. The goal of this project is to improve the physics education in colleges and universities. To solve the problem we created a demonstration system that effectively demonstrates an inertial reference frame and Newton's second law by driving a specially designed train with constant velocity or constant acceleration. Students are able to perform three different experiments which are controlled via a webpage or by remote control, and analyse the output through plotted graphs. After extensive testing, the train and experiments proved successful and we concluded that physics education can be effectively improved with the help of practical experiments that students themselves can perform.

  • 107.
    Karginova, Nadezda
    et al.
    Petrozavodsk University, Petrozavodsk, Russia.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Data-driven methods for classification of driving styles in buses2012Conference paper (Refereed)
    Abstract [en]

    Fuel consumption and vehicle breakdown depend upon the driving style of the driver, for example, hard driving style leads to more wear and consequently more failures of vehicle components. Because of this, it is important to identify and classify the driver’s driving style in order to give the driver feedback through a driver assistance system. The driver would then be able to detect and learn to avoid a driving style that is not appropriate. The input data is provided by different sensors installed in the vehicle, where different drivers and driving routes have been measured. The data is subjectively classified into two different driving styles: normal and hard. Hard driving style can be characterized, for example, by rapid acceleration and braking. Since it is not trivial to build a model which is able to distinguish hard driving from normal, a data mining approach has been employed. In the paper, several classifiers are compared (including e.g. neural networks and decision trees) and a discussion is made on the advantages and disadvantages of the different methods. Copyright © 2012 SAE International.

  • 108.
    Karlsson, Stefan M.
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Lip-motion events analysis and lip segmentation using optical flow2012Conference paper (Refereed)
    Abstract [en]

    We propose an algorithm for detecting the mouth events of opening and closing. Our method is translation and ro- tation invariant, works at very fast speeds, and does not re- quire segmented lips. The approach is based on a recently developed optical flow algorithm that handles the motion of linear structure in a stable and consistent way.Furthermore, we provide a semi-automatic tool for gen- erating groundtruth segmentation of video data, also based on the optical flow algorithm used for tracking keypoints at faster than 200 frames/second. We provide groundtruth for 50 sessions of speech of the XM2VTS database [16] avail- able for download, and the means to segment further ses- sions at a relatively small amount of user interaction.We use the generated groundtruth to test the proposed al- gorithm for detecting events, and show it to yield promising result. The semi-automatic tool will be a useful resource for researchers in need of groundtruth segmentation from video for the XM2VTS database and others.

  • 109.
    Khandelwal, Siddhartha
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gait Event Detection in the Real World2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Healthy gait requires a balance between various neuro-physiological systems and is considered an important indicator of a subject's physical and cognitive health status. As such, health-related applications would immensely benefit by performing long-term or continuous monitoring of subjects' gait in their natural environment and everyday lives. In contrast to stationary sensors such as motion capture systems and force plates, inertial sensors provide a good alternative for such gait analysis applications as they are miniature, cheap, mobile and can be easily integrated into wearable systems.

    This thesis focuses on improving overall gait analysis using inertial sensors by providing a methodology for detecting gait events in real-world settings. Although the experimental protocols for such analysis have been restricted to only highly-controlled lab-like indoor settings; this thesis presents a new gait database that consists of data from gait activities carried out in both, indoor and outdoor environments. The thesis shows how domain knowledge about gait could be formulated and utilized to develop methods that are robust and can tackle real-world challenges. It also shows how the proposed approach can be generalized to estimate gait events from multiple body locations. Another aspect of this thesis is to demonstrate that the traditionally used temporal error metrics are not enough for presenting the overall performance of gait event detection methods. The thesis introduces how non-parametric tests can be used to complement them and provide a better overview.

    The results of comparing the proposed methodology to state-of-the-art methods showed that the approach of incorporating domain knowledge into the time-frequency analysis of the signal was robust across different real-world scenarios and outperformed other methods, especially for the scenario involving variable gait speeds in outdoor settings. The methodology was also benchmarked on publicly available gait databases yielding good performance for estimating events from different body locations. To conclude, this thesis presents a road map for the development of gait analysis systems in real-world settings.

  • 110.
    Khandelwal, Siddhartha
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications2014In: 13th International Symposium on 3D Analysis of Human Movement: 14–17 July, 2014, Lausanne, Switzerland, 2014, , p. 4p. 151-154Conference paper (Refereed)
    Abstract [en]

    Detecting gait events is the key to many gait analysis applications which would immensely benefit if the analysis could be carried out using wearable sensors in uncontrolled outdoor environments, enabling continuous monitoring and long-term analysis. This would allow exploring new frontiers in gait analysis by facilitating the availability of more data and empower individuals, especially patients, to avail the benefits of gait analysis in their everyday lives. Previous gait event detection algorithms impose many restrictions as they have been developed from data collected incontrolled, indoor environments. This paper proposes a robust algorithm that utilizes a priori knowledge of gait in conjunction with continuous wavelet transform analysis, to accurately identify heel strike and toe off, from noisy accelerometer signals collected during indoor and outdoor walking. The accuracy of the algorithm is evaluated by using footswitches that are considered as ground truth and the results are compared with another recently published algorithm.

  • 111.
    Khandelwal, Siddhartha
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database2017In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 51, p. 84-90Article in journal (Refereed)
    Abstract [en]

    Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings.

    This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios. © 2016 Elsevier B.V.

  • 112.
    Khandelwal, Siddhartha
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis2016In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 24, no 12, p. 1363-1372Article in journal (Refereed)
    Abstract [en]

    Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments. © Copyright 2016 IEEE

  • 113.
    Khandelwal, Siddhartha
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations2018In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 59, p. 278-285Article in journal (Refereed)
    Abstract [en]

    Identifying Initial Contact events (ICE) is essential in gait analysis as they segment the walking pattern into gait cycles and facilitate the computation of other gait parameters. As such, numerous algorithms have been developed to identify ICE by placing the accelerometer at a specific body location. Simultaneously, many researchers have studied the effects of device positioning for participant or patient compliance, which is an important factor to consider especially for long-term studies in real-life settings. With the adoption of accelerometery for long-term gait analysis in daily living, current and future applications will require robust algorithms that can either autonomously adapt to changes in sensor positioning or can detect ICE from multiple sensors locations.

    This study presents a novel methodology that is capable of estimating ICE from accelerometers placed at different body locations. The proposed methodology, called DK-TiFA, is based on utilizing domain knowledge about the fundamental spectral relationships present between the movement of different body parts during gait to drive the time-frequency analysis of the acceleration signal. In order to assess the performance, DK-TiFA is benchmarked on four large publicly available gait databases, consisting of a total of 613 subjects and 7 unique body locations, namely, ankle, thigh, center waist, side waist, chest, upper arm and wrist. The DK-TiFA methodology is demonstrated to achieve high accuracy and robustness for estimating ICE from data consisting of different accelerometer specifications, varying gait speeds and different environments. © 2017 Elsevier B.V.

  • 114.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Luleå, Sweden.
    Hostettler, Roland
    Aalto University, Helsinki, Finland.
    Lyamin, Nikita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Birk, Wolfgang
    Luleå University of Technology, Luleå, Sweden.
    Wiklund, Urban
    Umea Univ, Dept Biomed Engn & Informat, Umea, Sweden..
    Osipov, Evgeny
    Luleå University of Technology, Luleå, Sweden.
    Vehicle Classification using Road Side Sensors and Feature-free Data Smashing Approach2016In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Piscataway: IEEE , 2016, p. 1988-1993, article id 7795877Conference paper (Refereed)
    Abstract [en]

    The main contribution of this paper is a study of the applicability of data smashing - a recently proposed data mining method - for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard, using measurements of road surface vibrations and magnetic field disturbances caused by passing vehicles. The main advantage of the studied classification approach is that it, in contrast to the most of traditional machine learning algorithms, does not require the extraction of features from raw signals. The proposed classification approach was evaluated on a large dataset consisting of signals from 3074 vehicles. Hence, a good estimate of the actual classification rate was obtained. The performance was compared to the previously reported results on the same problem for logistic regression. Our results show the potential trade-off between classification accuracy and classification method's development efforts could be achieved.

  • 115.
    Krish, Ram P.
    et al.
    School of Electronic Engineering, Dublin City University, Ireland.
    Fierrez, Julian
    School of Engineering, Universidad Autonoma de Madrid, Spain.
    Ramos, Daniel
    School of Engineering, Universidad Autonoma de Madrid, Spain.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Improving Automated Latent Fingerprint Identification Using Extended Minutia Types2019In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, p. 9-19Article in journal (Refereed)
    Abstract [en]

    Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features. © 2018 Elsevier B.V.

  • 116.
    Krish, Ram Prasad
    et al.
    Universidad Autonóma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ramos, Daniel
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ortega-Garcia, Javier
    Universidad Autonóma de Madrid, Madrid, Spain.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pre-registration of latent fingerprints based on orientation field2015In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 4, no 2, p. 42-52Article in journal (Refereed)
    Abstract [en]

    In this study, the authors present a hierarchical algorithm to register a partial fingerprint against a full fingerprint using only the orientation fields. In the first level, they shortlist possible locations for registering the partial fingerprint in the full fingerprint using a normalised correlation measure, taking various rotations into account. As a second level, on those candidate locations, they calculate three other similarity measures. They then perform score fusion for all the estimated similarity scores to locate the final registration. By registering a partial fingerprint against a full fingerprint, they can reduce the search space of the minutiae set in the full fingerprint, thereby improving the result of partial fingerprint identification, particularly for poor quality latent fingerprints. They report the rank identification improvements of two minutiae-based automated fingerprint identification systems on the National Institute of Standards and Technology (NIST)-Special Database 27 database when they use the authors hierarchical registration as a pre-alignment. © The Institution of Engineering and Technology 2015.

  • 117.
    Lundström, Jens
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Situation Awareness in Colour Printing and Beyond2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Machine learning methods are increasingly being used to solve real-world problems in the society. Often, the complexity of the methods are well hidden for users. However, integrating machine learning methods in real-world applications is not a straightforward process and requires knowledge both about the methods and domain knowledge of the problem. Two such domains are colour print quality assessment and anomaly detection in smart homes, which are currently driven by manual monitoring of complex situations. The goal of the presented work is to develop methods, algorithms and tools to facilitate monitoring and understanding of the complex situations which arise in colour print quality assessment and anomaly detection for smart homes. The proposed approach builds on the use and adaption of supervised and unsupervised machine learning methods.

    Novel algorithms for computing objective measures of print quality in production are proposed in this work. Objective measures are also modelled to study how paper and press parameters influence print quality. Moreover, a study on how print quality is perceived by humans is presented and experiments aiming to understand how subjective assessments of print quality relate to objective measurements are explained. The obtained results show that the objective measures reflect important aspects of print quality, these measures are also modelled with reasonable accuracy using paper and press parameters. The models of objective  measures are shown to reveal relationships consistent to known print quality phenomena.

    In the second part of this thesis the application area of anomaly detection in smart homes is explored. A method for modelling human behaviour patterns is proposed. The model is used in order to detect deviating behaviour patterns using contextual information from both time and space. The proposed behaviour pattern model is tested using simulated data and is shown to be suitable given four types of scenarios.

    The thesis shows that parts of offset lithographic printing, which traditionally is a human-centered process, can be automated by the introduction of image processing and machine learning methods. Moreover, it is concluded that in order to facilitate robust and accurate anomaly detection in smart homes, a holistic approach which makes use of several contextual aspects is required.

  • 118.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of people in their own homesManuscript (preprint) (Other academic)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

  • 119.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of smart home residents2016In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, p. 429-440Article in journal (Refereed)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

    Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

    The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

  • 120.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Ourique de Morais, Wagner
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Cooney, Martin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    A Holistic Smart Home Demonstrator for Anomaly Detection and Response2015In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 330-335Conference paper (Refereed)
    Abstract [en]

    Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve.In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios. © IEEE 2015

  • 121.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Synnott, Jonathan
    Ulster University, Jordanstown, United Kingdom.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nugent, Christopher
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Ulster University, Jordanstown, United Kingdom.
    Smart Home Simulation using Avatar Control and Probabilistic Sampling2015In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 336-341Conference paper (Refereed)
    Abstract [en]

    Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval. © IEEE 2015

  • 122.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    System for Assessing, Exploring and Monitoring Offset Print Quality2011In: Recent Researches in Circuits, Systems, Communications & Computers: Proc. of 2nd European Conference of Communications (ECCOM'11), Athens: World Scientific and Engineering Academy and Society, 2011, p. 28-33Conference paper (Refereed)
    Abstract [en]

    Variations in offset print quality relate to numerous parameter of printing press and paper. To maintain constant quality of products, press operators need to assess, explore and monitor print quality. This paper presents a novel system for assessing and predicting values of print quality attributes, where the adopted, random forests (RF)-based, modeling approach also allows quantifying the influence of different parameters. In contrast to other print quality assessment systems, this system utilizes common print marks known as double grey-bars. A novel virtual sensor for assessing the mis-registration degree of printing plates using images of double grey-bars is presented. The inferred influence of paper and printing press parameters on print quality shows correlation with known print quality conditions.

  • 123.
    Madagalam, Mallikarjun
    Halmstad University, School of Information Technology. Lund University.
    Pulse Energy Stabilization of a Femtosecond Laser Pulse Chain2016Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
  • 124.
    Malmberg, Donald
    et al.
    Mefos Met Res Plant, Lulea, Sweden .
    Björkvall, J.
    Mefos Met Res Plant, Lulea, Sweden .
    Malm, J.
    Mefos Met Res Plant, Lulea, Sweden .
    Bååth, Lars B.
    Halmstad University, School of Business and Engineering (SET).
    Preliminary microwave measurements on liquid stags2005In: Ironmaking & steelmaking, ISSN 0301-9233, E-ISSN 1743-2812, Vol. 32, no 1, p. 61-67Article in journal (Refereed)
    Abstract [en]

    Microwave technology has for decades been a tool for astronomers in their work to map and understand the complexities of the universe in terms of composition and extent but it is also used at laboratory scale by spectroscopists to examine the properties of atomic and molecular compounds. This paper discusses the use of microwave technology for the investigation of liquid slag structures. Preliminary results indicate that alteration of slag composition could be correlated to the measured microwave refractive index. Investigations have been performed on Al2 O3 -CaO-SiO2.

  • 125.
    Marinho, Marco A. M.
    et al.
    University of Brasília (UnB), Brasília, Brazil & German Aerospace Center (DLR), Cologne, Germany.
    da Costa, João Paulo C. L.
    University of Brasília (UnB), Brasília, Brazil; llmenau University of Technology, Ilmenau, Germany & Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany.
    Antreich, Felix
    German Aerospace Center (DLR), Cologne, Germany.
    de Almeida, André L. F.
    Federal University of Ceará (UFC), Fortaleza, Brazil.
    Del Galdo, Giovanni
    llmenau University of Technology, Ilmenau, Germany & Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany.
    de Freitas, Edison Pignaton
    University of Brasília (UnB), Brasília, Brazil & Informatics Institute, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Array interpolation based on multivariate adaptive regression splines2016In: 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper (Refereed)
    Abstract [en]

    Many important signal processing techniques such as Spatial Smoothing, Forward Backward Averaging and Root-MUSIC, rely on antenna arrays with specific and precise structures. Arrays with such ideal structures, such as a centro-hermitian structure, are often hard to build in practice. Array interpolation is used to enable the usage of these techniques with imperfect (not having a centro-hermitian structure) arrays. Most interpolation methods rely on methods based on least squares (LS) to map the output of a perfect virtual array based on the real array. In this work, the usage of Multivariate Adaptive Regression Splines (MARS) is proposed instead of the traditional LS to interpolate arrays with responses largely different from the ideal.

  • 126.
    Marinho, Marco A. M.
    et al.
    Halmstad University, School of Information Technology. University of Brasília (UnB), Department of Electrical Engineering (ENE), Brasília, Brazil.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Tufvesson, Fredrik
    Department of Electrical and Information Technology, Lund University, Lund, Sweden.
    Antreich, Felix
    Department of Teleinformatics Engineering - Federal University of Ceará (UFC), Fortaleza, Brazil.
    Da Costa, João Paulo C.L.
    University of Brasília (UnB), Department of Electrical Engineering (ENE), Brasília, Brazil.
    Performance Assessment for Distributed Broadband Radio Localization2018In: 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Washington, DC: IEEE, 2018, p. 20-23Conference paper (Refereed)
    Abstract [en]

    Various emerging technologies, such as autonomous vehicles and fully autonomous flying, require precision positioning. This work presents a localization and tracking method based on joint direction of arrival (DOA), time delay, and range estimation using the SAGE algorithm. The proposed method does not rely on external sources of information such as global navigation satellite systems (GNSS). The method is opportunistic and does not require any location-based data exchange. A set of numerical simulations is presented to assess the performance of the proposed method.

  • 127.
    Marinho, Marco
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS). University of Brasília (UnB), Department of Electrical Engineering (ENE), Brasília, Brazil.
    Antreich, Felix
    Department of Teleinformatics Engineering Federal University of Ceará (UFC), Fortaleza, Brazil.
    da Costa, João Paulo C.L.
    University of Brasília (UnB), Department of Electrical Engineering (ENE), Brasília, Brazil.
    Caizzone, Steffano
    German Aerospace Center (DLR), Institute for Communications and Navigation, Wessling, Germany.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Pignaton de Freitas, Edison
    Federal University of Rio Grande do Sul (UFRGS), Informatics Institute, Porto Alegre, Brazil.
    Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals2018In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 144, p. 19-28Article in journal (Refereed)
    Abstract [en]

    Important signal processing techniques need that the response of the different elements of a sensor array has specific characteristics. For physical systems this often is not achievable as the array elements’ responses are affected by mutual coupling or other effects. In such cases, it is necessary to apply array interpolation to allow the application of ESPRIT, Forward Backward Averaging (FBA), and Spatial Smoothing (SPS). Array interpolation provides a model or transformation between the true and a desired array response. If the true response of the array becomes more distorted with respect to the desired one or the considered region of the field of view of the array increases, nonlinear approaches becomes necessary. This work presents two novel methods for sector discretization. An Unscented Transform (UT) based method and a principal component analysis (PCA) based method are discussed. Additionally, two novel nonlinear interpolation methods are developed based on the nonlinear regression schemes Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). These schemes are extended and applied to the array interpolation problem. The performance of the proposed methods is examined using simulated and measured array responses of a physical system used for research on mutual coupling in antenna arrays. © 2017 The Author(s). Published by Elsevier B.V.

  • 128.
    Marques Marinho, Marco Antonio
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Array Processing Techniques for Direction of Arrival Estimation, Communications, and Localization in Vehicular and Wireless Sensor Networks2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Array signal processing in wireless communication has been a topic of interest in research for over three decades. In the fourth generation (4G) of the wireless communication systems, also known as Long Term Evolution (LTE), multi antenna systems have been adopted according to the Release 9 of the 3rd Generation Partnership Project (3GPP). For the fifth generation (5G) of the wireless communication systems, hundreds of antennas should be incorporated to the devices in a massive multi-user Multiple Input Multiple Output (MIMO) architecture. The presence of multiple antennas provides array gain, diversity gain, spatial gain, and interference reduction. Furthermore, arrays enable spatial filtering and parameter estimation, which can be used to help solve problems that could not previously be addressed from a signal processing perspective. The aim of this thesis is to bridge some gaps between signal processing theory and real world applications. Array processing techniques traditionally assume an ideal array. Therefore, in order to exploit such techniques, a robust set of methods for array interpolation are fundamental and are developed in this work. In this dissertation, novel methods for array interpolation are presented and their performance in real world scenarios is evaluated. Problems in the field of wireless sensor networks and vehicular networks are also addressed from an array signal processing perspective. Signal processing concepts are implemented in the context of a wireless sensor network. These concepts provide a level of synchronization sufficient for distributed multi antenna communication to be applied, resulting in improved lifetime and improved overall network behaviour. Array signal processing methods are proposed to solve the problem of radio based localization in vehicular network scenarios with applications in road safety and pedestrian protection.

  • 129.
    Martinez-Diaz, M.
    et al.
    Universidad Autonoma de Madrid, Spain.
    Fierrez-Aguilar, J.
    Universidad Autonoma de Madrid, Spain.
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Ortega-Garcia, J.
    Universidad Autonoma de Madrid, Spain.
    Siguenza, J. A.
    Universidad Autonoma de Madrid, Spain.
    Hill-climbing and brute-force attacks on biometric systems: A case study in Match-on-Card fingerprint verification2006In: Proceedings 2006: 40th Annual IEEE International Carnahan Conferences Security Technology, October 16-19, 2006, Lexington, Kentucky, Piscataway, N.J.: IEEE Press, 2006, p. 151-159Conference paper (Refereed)
    Abstract [en]

    In this paper, we study the robustness of state-of-theart automatic fingerprint verification systems against hill-climbing and brute-force attacks. We compare the performance of this type of attacks against two different minutiae-based systems, the NIST Fingerprint Image Software 2 (NFIS2) reference system and a Match-on-Card based system. In order to study their success rate, the attacks are analyzed and modified in each scenario. We focus on the influence of initial conditions in hill-climbing attacks, like the number of minutiae in the synthetically generated templates or the performance of each type of modification in the template. We demonstrate how slight modifications in the hill-climbing algorithm lead to very different success rates. © 2006 IEEE.

  • 130.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Fan, Yuantao
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Hand Detection and Gesture Recognition Using Symmetric Patterns2016In: Studies in Computational Intelligence, ISSN 1860-949X, E-ISSN 1860-9503, Vol. 642, p. 365-375Article in journal (Other academic)
    Abstract [en]

    Hand detection and gesture recognition is one of the challenging issues in human-robot interaction. In this paper we proposed a novel method to detect human hands and recognize gestures from video stream by utilizing a family of symmetric patterns: log-spiral codes. In this case, several log-family spirals mounted on a hand glove were extracted and utilized for positioning the palm and fingers. The proposed method can be applied in real time and even on a low quality camera stream. The experiments are implemented in different conditions to evaluate the illumination, scale, and rotation invariance of the proposed method. The results show that using the proposed technique we can have a precise and reliable detection and tracking of the hand and fingers with accuracy about 98 %. © Springer International Publishing Switzerland 2016.

  • 131.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gholami Shahbandi, Saeed
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Human Tracking in Occlusion based on Reappearance Event Estimation2016In: ICINCO 2016: 13th International Conference on Informatics in Control, Automation and Robotics: Proceedings, Volume 2 / [ed] Oleg Gusikhin, Dimitri Peaucelle & Kurosh Madani, SciTePress, 2016, Vol. 2, p. 505-512Conference paper (Refereed)
    Abstract [en]

    Relying on the commonsense knowledge that the trajectory of any physical entity in the spatio-temporal domain is continuous, we propose a heuristic data association technique. The technique is used in conjunction with an Extended Kalman Filter (EKF) for human tracking under occlusion. Our method is capable of tracking moving objects, maintain their state hypothesis even in the period of occlusion, and associate the target reappeared from occlusion with the existing hypothesis. The technique relies on the estimation of the reappearance event both in time and location, accompanied with an alert signal that would enable more intelligent behavior (e.g. in path planning). We implemented the proposed method, and evaluated its performance with real-world data. The result validates the expected capabilities, even in case of tracking multiple humans simultaneously.

  • 132.
    Menezes, Maria Luiza Recena
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Methodology for Subject Authentification and Identification through EEG signal: equipment's and positioning artifacts2018In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 37-37Conference paper (Refereed)
  • 133.
    Menezes, Maria Luiza Recena
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pavel, Misha
    Northeastern University, Boston, USA.
    Jimison, Holly
    Northeastern University, Boston, USA.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Affective Ambient Intelligence: from Domotics to Ambient Intelligence2018In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 25-25Conference paper (Refereed)
  • 134.
    Menezes, Maria Luiza Recena
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Samara, A.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Galway, L.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wang, H.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Bond, R.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset2017In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 21, no 6, p. 1003-1013Article in journal (Refereed)
    Abstract [en]

    One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s).

  • 135.
    Midtiby, Henrik Skov
    et al.
    The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Jørgensen, Ole
    Operations Management, Aarhus University, Tjele, Denmark.
    Jørgensen, Rasmus Nyholm
    Signal Processing, Aarhus University, Aarhus, Denmark.
    Upper limit for context-based crop classification in robotic weeding applications2016In: Biosystems Engineering, ISSN 1537-5110, E-ISSN 1537-5129, Vol. 146, p. 183-192Article in journal (Refereed)
    Abstract [en]

    Knowledge of the precise position of crop plants is a prerequisite for effective mechanical weed control in robotic weeding application such as in crops like sugar beets which are sensitive to mechanical stress. Visual detection and recognition of crop plants based on their shapes has been described many times in the literature. In this paper the potential of using knowledge about the crop seed pattern is investigated based on simulated output from a perception system. The reliability of position–based crop plant detection is shown to depend on the weed density (ρ, measured in weed plants per square metre) and the crop plant pattern position uncertainty (σx and σy, measured in metres along and perpendicular to the crop row, respectively). The recognition reliability can be described with the positive predictive value (PPV), which is limited by the seeding pattern uncertainty and the weed density according to the inequality: PPV ≤ (1 + 2πρσxσy)−1. This result matches computer simulations of two novel methods for position–based crop recognition as well as earlier reported field–based trials. © 2016 IAgrE

  • 136.
    Mikaelyan, Anna
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Periocular Recognition by Detection of Local Symmetry Patterns2014In: Proceedings: Tenth International Conference on Signal-Image Technology and Internet-Based System: 23–27 November 2014: Marrakech, Morocco / [ed] Kokou Yetongnon, Albert Dipanda & Richard Chbeir, Los Alamitos, CA: IEEE Computer Society, 2014, p. 584-591Conference paper (Refereed)
    Abstract [en]

    We present a new system for biometric recognition using periocular images. The feature extraction method employed describes neighborhoods around keypoints by projection onto harmonic functions which estimates the presence of a series of various symmetric curve families around such keypoints. The iso-curves of such functions are highly symmetric w.r.t. the keypoints and the estimated coefficients have well defined geometric interpretations. The descriptors used are referred to as Symmetry Assessment by Feature Expansion (SAFE). Extraction is done across a set of discrete points of the image, uniformly distributed in a rectangular-shaped grid positioned in the eye center. Experiments are done with two databases of iris data, one acquired with a close-up iris camera, and another in visible light with a webcam. The two databases have been annotated manually, meaning that the radius and center of the pupil and sclera circles are available, which are used as input for the experiments. Results show that this new system has a performance comparable with other periocular recognition approaches. We particularly carry out comparative experiments with another periocular system based on Gabor features extracted from the same set of grid points, with the fusion of the two systems resulting in an improved performance. We also evaluate an iris texture matcher, providing fusion results with the periocular systems as well.

  • 137.
    Mikaelyan, Anna
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Frequency and ridge estimation using structure tensor2013In: Proceedings of Biometric Technologies in Forensic Science: Nijmegen, 14–15 October 2013, Nijmegen: Radboud University Nijmegen , 2013, p. 58-59Conference paper (Refereed)
  • 138.
    Mikaelyan, Anna
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    SAFE features for matching fingermarks by neighbourhoods of single minutiae2014In: 2014 14th International Symposium on Communications and Information Technologies (ISCIT), Piscataway, N.J.: IEEE Press, 2014, p. 181-185, article id 7011896Conference paper (Refereed)
    Abstract [en]

    Symmetry Assessment by Finite Expansion (SAFE) is a novel description of image information by means of Generalized Structure Tensor. It represents orientation data in neighbourhood of key points projected onto the space of harmonic functions creating a geometrically interpretable feature of low dimension. The proposed feature has built in quality metrics reflecting accuracy of the extracted feature and ultimately the quality of the key point. The feature vector is orientation invariant in that it is orientation steerable with low computational cost. We provide experiments on minutia key points of forensic fingerprints to demonstrate its usefulness. Matching is performed based on minutia in regions with high orientation variance, e.g. in proximity of core points. Performance of single matching minutia equals to 20% EER and Rank-20 CMC 69% on the only publicly available annotated forensic fingerprint SD27 database.

    Further, we complement SAFE descriptors of orientation maps with SAFE descriptors of frequency features in a similar manner. In case of combined features the performance is improved further to 19% EER and 74% Rank-20 CMC. © 2014 IEEE.

  • 139.
    Mikaelyan, Anna
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Symmetry Assessment by Finite Expansion: application to forensic fingerprints2014In: 2014 International Conference of the Biometrics Special Interest Group (BIOSIG) / [ed] Arslan Brömme & Christoph Busch, Bonn: Gesellschaft für Informatik, 2014, p. 87-98Conference paper (Refereed)
    Abstract [en]

    Common image features have too poor information for identification of forensic images of fingerprints, where only a small area of the finger is imaged and hence a small amount of key points are available. Noise, nonlinear deformation, and unknown rotation are additional issues that complicate identification of forensic fingerprints. We propose a feature extraction method which describes image information around key points: Symmetry Assessment by Finite Expansion (SAFE). The feature set has built-in quality estimates as well as a rotation invariance property. The theory is developed for continuous space, allowing compensation for features directly in the feature space when images undergo such rotation without actually rotating them. Experiments supporting that use of these features improves identification of forensic fingerprint images of the public NIST SD27 database are presented. Performance of matching orientation information in a neighborhood of core points has an EER of 24% with these features alone, without using minutiae constellations, in contrast to 36% when using minutiae alone. Rank-20 CMC is 58%, which is lower than 67% when using notably more manually collected minutiae information.

  • 140.
    Minelga, Jonas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Padervinskis, Evaldas
    Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Comparing Throat and Acoustic Microphones for Laryngeal Pathology Detection from Human Voice2014In: Electrical and Control Technologies: Proceedings of the 9th International Conference on Electrical and Control Technologies ECT-2014 / [ed] A. Navickas (general editor), A. Sauhats, A. Virbalis, M. Ažubalis, V. Galvanauskas, K. Brazauskas & A. Jonaitis, Kaunas: Kaunas University of Technology , 2014, p. 50-53Conference paper (Refereed)
    Abstract [en]

    The aim of this study was to compare acoustic and throat microphones in the voice pathology detection task. Recordings of sustained phonation /a/ were used in the study. Each recording was characterized by a rather large set of diverse features, 1051 features in total. Classification into two classes, namely normal and pathological, was performed using random forest committees. Models trained using data obtained from the throat microphone provided lower classification accuracy. This is probably due to a narrower frequency range of the throat microphone leading to loss of important information. © Kaunas University of Technology, 2014.

  • 141.
    Molchanov, Pavlo
    et al.
    Tampere University of Technology, Tampere, Finland.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). Tampere University of Technology, Tampere, Finland.
    Astola, Jaakko
    Tampere University of Technology, Tampere, Finland.
    Egiazarian, Karen
    Tampere University of Technology, Tampere, Finland.
    Radar frequency band invariant pedestrian classification2013In: International Radar Symposium, IRS 2013: Proceedings, Volume II / [ed] Hermann Rohling, Göttingen, Germany: Cuvillier Verlag, 2013, p. 740-745Conference paper (Refereed)
    Abstract [en]

    The problem of pedestrian classification by radars with different operating bands is considered. The proposed solutions are based on two observations of relation between Doppler spectrums collected within different frequency bands. According to the first observation the Doppler spectrum obtained from the radars with lower operating frequency is an approximate scaled version of the Doppler spectrum obtained within radar with higher operating frequency. According to the second observation the Doppler spectrum of lower operating frequency is similar to the Doppler spectrum of higher operating frequency at specific aspect angle. Two new approaches are proposed for pedestrian classification based on the observations. The first proposed approach deals with new features called local binary mask. The second approach is based on higher order spectral invariants estimated from the Doppler spectrum. The approaches are tested on real 24 GHz radar measurements and simulated 77 GHz radar measurements and show the robustness to operating frequency of the radar. © 2013 German Inst of Navigation.

  • 142.
    Muhammad, Naveed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Intention Estimation Using Set of Reference Trajectories as Behaviour Model2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 12, article id 4423Article in journal (Refereed)
    Abstract [en]

    Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors

  • 143.
    Muhammad, Naveed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 19, article id 4279Article in journal (Refereed)
    Abstract [en]

    As human drivers, we instinctively employ our understanding of other road users' behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction.

  • 144.
    Mühlfellner, Peter
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems2011Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    We propose a scalable and fexible hardware architecture for the extraction of image features, used in conjunction with an attentional cascade classifier for appearance-based object detection. Individual feature processors calculate feature-values in parallel, using parameter-sets and image data that is distributed via BRAM buffers. This approach can provide high utilization- and throughput-rates for a cascade classifier. Unlike previous hardware implementations, we are able to flexibly assign feature processors to either work on a single- or multiple image windows in parallel, depending on the complexity of the current cascade stage.

    The core of the architecture was implemented in the form of a streaming based FPGA design, and validated in simulation, synthesis, as well as via the use of a Logic Analyser for the verification of the on-chip functionality. For the given implementation, we focused on the design of Haar-like feature processors, but feature processors for a variety of heterogenous feature types, such as Gabor-like features, can also be accomodated by the proposed hardware architecture.

  • 145.
    Nelson, Christian
    et al.
    Lund University, Lund, Sweden.
    Lyamin, Nikita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Gustafson, Carl
    Lund University, Lund, Sweden.
    Tufvesson, Fredrik
    Lund University, Lund, Sweden.
    Geometry Based Channel Models with Cross- and Autocorrelation for Vehicular Network Simulations2018In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Piscataway, NJ: IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Realistic network simulations are necessary to assess the performance of any communication system. In this paper, we describe an implementation of a channel model for vehicle-to-vehicle (V2V) communication in the OMNeT++/Plexe simulation environment. The model is based on previous extensive measurements in a V2V multilink highway scenario and cover line-of-sight (LOS) as well as obstructed LOS (OLOS) scenarios, which occurs when one or more vehicles obstruct the LOS component. The implementation captures both the temporal autocorrelation and the joint multilink cross-correlation processes to achieve a realistic behavior. Preliminary results show that the implementation now generates stochastic large-scale fading with an autocorrelation function that agrees well with measured data. A representation of the cross-correlation process is now implemented through proper channel model selection since the geometry and location of objects are known in Plexe. We also show the impact of the suggested V2V physical layer (PHY) on the performance evaluation results observed at the facilities layer. As a metric, we use the data age, which is a measure how old the information about a vehicle is. When considering the autocorrelation in simulations, the experienced data-age increases. Examples show an increase of the 10% percentile data-age from 0.1s to 1.5s, which may affect the application performance significantly in critical situations. © 2018 IEEE.

  • 146.
    Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Tracking of People in Paper Mill Warehouse Using Laser Range Sensor2014In: UKSim-AMSS Eighth European Modelling Symposium on Computer Modelling and Simulation, EMS 2014 / [ed] David Al-Dabass, Valentina Colla, Marco Vannucci & Athanasios Pantelous, Los Alamitos, CA: IEEE Computer Society, 2014, p. 52-57, article id 7153974Conference paper (Refereed)
    Abstract [en]

    In this paper a laser scanner based approach for simultaneous detection and tracking of people in an indoor environment is presented. The operation of an autonomous truck, for transporting paper reels in a dynamic environment shared with humans, is considered as the application setting for this work. Here, a human leg detection procedure and an Extended Kalman Filter (EKF) based tracking method are employed for real-time performance. Several experiments with different data sets collected from an autonomous forklift truck in a paper mill warehouse have been performed in an offline situation. The results show how the system is able to detect and track multiple moving people. ©2014 IEEE.

  • 147.
    Nilsson, Emil
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), MPE-lab.
    Svensson, Christer
    Linkoping Univ, Dept Elect Engn ISY, SE-58183 Linkoping, Sweden..
    Ultra Low Power Wake-Up Radio Using Envelope Detector and Transmission Line Voltage Transformer2013In: IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, ISSN 2156-3357, Vol. 3, no 1, p. 5-12Article in journal (Refereed)
    Abstract [en]

    An ultra-low power wake-up radio receiver using no oscillators is described. The radio utilizes an envelope detector followed by a baseband amplifier and is fabricated in a 130-nm complementary metal-oxide-semiconductor process. The receiver is preceded by a passive radio-frequency voltage transformer, also providing 50 Omega antenna matching, fabricated as transmission lines on the FR4 chip carrier. A sensitivity of -47 dBm with 200 kb/s on-off keying modulation is measured at a current consumption of 2.3 mu A from a 1 V supply. No trimming is used. The receiver accepts a dBm continuous wave blocking signal, or modulated blockers 6 dB below the sensitivity limit, with no loss of sensitivity.

  • 148.
    Nilsson, Kenneth
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Prominent symmetry points as landmarks in fingerprint images for alignment2002In: 16th International Conference on Pattern Recognition (ICPR'02) - Proceedings, Volume 3, Piscataway: IEEE Computer Society, 2002, Vol. III, p. 395-398Conference paper (Other academic)
    Abstract [en]

    For the alignment of two fing erprints position of certain landmarks are needed. These should be automatically extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (core-points) in the fing erprint. They are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filter s, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

  • 149.
    Nytorpe Piledahl, Staffan
    et al.
    Halmstad University, School of Information Technology.
    Dahlberg, Daniel
    Halmstad University, School of Information Technology.
    Detektering av stress från biometrisk data i realtid2016Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    At the time of writing, stress and stress related disease have become the most common reasons for absence in the workplace in Sweden. The purpose of the work presented here is to identify and notify people managing unhealthy levels of stress. Since symptoms of mental stress manifest through functions of the Sympathetic Nervous System (SNS), they are best measured through monitoring of SNS changes and phenomena. In this study, changes in the sympathetic control of heart rate were recorded and analyzed using heart rate variability analysis and a simple runner’s heart rate sensor connected to a smartphone.

    Mental stress data was collected through stressful video gaming. This was compared to data from non-stressful activities, physical activity and extremely stressful activities such as public speaking events. By using the period between heartbeats and selecting features from the frequency domain, a simple machine learning algorithm could differentiate between the types of data and thus could effectively recognize mental stress.

    The study resulted in a collection of 100 data points, an algorithm to extract features and an application to continuously collect and classify sequences of heart periods. It also revealed an interesting relationship in the data between different subjects.

    The fact that continuous stress monitoring can be achieved using minimally intrusive sensors is the greatest benefit of these results, especially when connsidering its potential value in the identification and prevention of stress related disease.

  • 150. Ortega-Garcia, Javier
    et al.
    Fierrez, Julian
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Galbally, J.
    Freire, M. R.
    Gonzalez-Rodriguez, J.
    Garcia-Mateo, C.
    Alba-Castro, J. -L
    Gonzalez-Agulla, E.
    Otero-Muras, E.
    Garcia-Salicetti, S.
    Allano, L.
    Ly-Van, B.
    Dorizzi, B.
    Kittler, J.
    Bourlai, T.
    Poh, N.
    Deravi, F.
    Ng, M. W. R.
    Fairhurst, M.
    Hennebert, J.
    Humm, A.
    Tistarelli, M.
    Brodo, L.
    Richiardi, J.
    Drygajlo, A.
    Ganster, H.
    Sukno, F. M.
    Pavani, S. -K
    Frangi, A.
    Akarun, L.
    Savran, A.
    The Multi-Scenario Multi-Environment BioSecure Multimodal Database (BMDB)2009In: IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 32, no 6, p. 1097-1111Article in journal (Refereed)
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