Change search
Refine search result
1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Berg, H.
    et al.
    Olsson, R.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Chilo, J.
    Automatic design of pulse coupled neurons for image segmentation2008In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 71, no 10-12, p. 1980-1993Article in journal (Refereed)
    Abstract [en]

    Automatic Design of Algorithms through Evolution (ADATE) is a program synthesis system that creates recursive programs in a functional language with automatic invention of recursive help functions and self-adaptive optimization of numerical values. We implement a neuron in a pulse coupled neural network (PCNN) as a recursive function in the ADATE language and then use ADATE to automatically evolve better PCNN neurons for image segmentation. Our technique is generally applicable for automatic improvement of most image processing algorithms and neural computing methods. It may be used either to generally improve a given implementation or to tailor that implementation to a specific problem, which with respect to image segmentation for example can be road following for autonomous vehicles or infrared image segmentation for heat seeking missiles that are to distinguish the heat source of the target from flares.

  • 2. Chilo, J.
    et al.
    Horvath, G.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Olsson, R.
    Electronic nose ovarian carcinoma diagnosis based on machine learning algorithms2009In: Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings, Springer, 2009, p. 13-23Conference paper (Refereed)
    Abstract [en]

    Ovarian carcinoma is one of the most deadly diseases, especially in the case of late diagnosis. This paper describes the result of a pilot study on an early detection method that could be inexpensive and simple based on data processing and machine learning algorithms in an electronic nose system. Experimental analysis using real ovarian carcinoma samples is presented in this study. The electronic nose used in this pilot test is very much the same as a nose used to detect and identify explosives. However, even if the apparatus used is the same, it is shown that the use of proper algorithms for analysis of the multi-sensor data from the electronic nose yielded surprisingly good results with more than 77% classification rate. These results are suggestive for further extensive experiments and development of the hardware as well as the software.

  • 3. Chilo, J.
    et al.
    Horvath, G.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Olsson, R.
    Redeby, Johan
    KTH, School of Chemical Science and Engineering (CHE), Chemistry, Analytical Chemistry (closed 20110630).
    Roeraade, Johan
    KTH, School of Chemical Science and Engineering (CHE), Chemistry, Analytical Chemistry (closed 20110630).
    A flexible electronic nose for odor discrimination using different methods of classification2009In: 2009 16th IEEE-NPSS Real Time Conference - Conference Record, 2009, p. 317-320Conference paper (Refereed)
    Abstract [en]

    Ovarian cancer is one of the leading causes of death from cancer in women. The lifetime risk is around 1.5%, which makes it the second most common gynecologic malignancy (the first one being breast cancer). To have a definitive diagnose, a surgical procedure is generally required and suspicious areas (samples) will be removed and sent for microscopic and other analysis. This paper describes the result of a pilot study in which an electronic nose is used to "smell" the aforementioned samples, analyze the multi-sensor signals and have a close to real-time answer on the detection of cancer. Besides being fast, the detection method is inexpensive and simple. Experimental analysis using real ovarian carcinoma samples shows that the use of proper algorithms for analysis of the multi-sensor data from the electronic nose yielded surprisingly good results with more than 77% classification rate. The electronic nose used in this pilot study was originally developed to be used as a "bomb dog" and can distinguish between e.g. TNT, Dynamex, Prillit. However, it was constructed to be a flexible multi-sensor device and the individual (16) sensors can easily be replaced/exchanged. This is suggestive for further investigations to obtain even better results with new, specific sensors. In another pilot experiment, headspace of an ovarian carcinoma sample and a control sample were analyzed using gas chromatography-mass spectrometry. Significant differences in chemical composition and compound levels were recorded, which would explain the different response obtained with the electronic nose.

  • 4. Chilo, Josè
    et al.
    Jabor, Abbas
    KTH, School of Engineering Sciences (SCI), Physics.
    Liszka, L.
    Eide, Å.J.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Persson, L.
    Infrasonic and Seismic Signals from Earthquake and Explosions in Arequipa, Perú2006In: Western Pacific Geophysics Meeting. 24-27 July 2006, Beijing, China, 2006Conference paper (Refereed)
  • 5.
    Chilo, José
    et al.
    University of Gävle.
    Jabor, Abbas
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    et al.,
    Filtering and extracting features from infrasound data2005In: 2005 14TH IEEE-NPSS Real Time Conference: Stockholm; 4 June 2005 through 10 June 2005, 2005, p. 451-455Conference paper (Refereed)
    Abstract [en]

    There are many reasons for using infrasound, i.e. low frequency sound, to monitor various events. Inherent features like its long-distance propagation and the use of simple, ground based equipment in very flexible system are some. The disadvantage is that it is a slow system due to the speed of sound. In this papr we try to show that there are several other advantages if one can extract all the features of the signal. In this way it is hoped that we can get a fingerprint of the event that caused the infrasound. Rayleigh waves and sound from epicentre may be obtained for earthquakes, pressure pulses and electro jets from aurora, core radius and funnel shape from tornados, etc. All these possibilities are suggestive for further R&D of the infrasound detection systems.

  • 6.
    Chilo, José
    et al.
    University of Gävle.
    Jabor, Abbas
    KTH, School of Engineering Sciences (SCI), Physics.
    Lizska, Ludwik
    Swedish Institute of Space Physics in Umeå.
    Eide, Åge J.
    Ostfold University College.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Obtaining "images" from iron objects using a 3-axis fluxgate magnetometer2007In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 580, no 2, p. 1105-1109Article in journal (Refereed)
    Abstract [en]

    Magnetic objects can cause local variations in the Earth's magnetic field that can be measured with a magnetometer. Here we used triaxial magnetometer measurements and an analysis method employing wavelet techniques to determine the "signature" or "fingerprint" of different iron objects. Clear distinctions among the iron samples were observed. The time-dependent changes in the frequency powers were extracted by use of the Morlet wavelet corresponding to frequency bands from 0.1 to 100 Hz. (c) 2007 Elsevier B.V. All rights reserved.

  • 7.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Kinser, Jason M.
    George Mason University.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Discrimination of nuclear explosions sites by seismic signals using intrinsic mode functions and multi-modal data space2008In: 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings; Boston, MA; 6 July 2008 through 11 July 2008, 2008, p. II895-II898Conference paper (Refereed)
    Abstract [en]

    Signal processing and feature extraction are investigated using the Empirical Mode Decomposition (EMD). It is believed that this approach is well suited for non-linear and non-stationary data. With EMD any complicated set of data can be decomposed into a finite, and usually small number, of functions called Intrinsic Mode Functions (IMFs). A new discriminating system is presented here that is capable of discriminating between different seismic signals from nuclear testing sites based on the IMFs and the multi-modal data space. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space. This space is capable of extracting similarities among several signals through a combination of multiple metrics. This is a new way of associating data. After illustrating the technique with an investigation of an audio data example (piano), we examine the characteristics of seismic signals from nuclear testing (explosions). The results presented in this paper indicate that a relatively simple discriminating system can successfully cluster and classify seismic events.

  • 8.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    A low cost digital data acquisition system for infrasonic records2007In: IDAACS 2007: Proceedings Of The 4th IEEE Workshop On Intelligent Data Acquisition And Advanced Computing Systems: Technology And Applications, 2007, p. 35-37Conference paper (Refereed)
    Abstract [en]

    This paper describes a new digital data acquisition system that can be used to record signals from infrasound events. The system includes a QF4A512 programmable signal converter from Quickfilter Technologies and a MSP430 microcontroller from Texas Instruments. The signal output of the infrasound sensors is converted to digital via a 16-bits Analog to Digital Converter (ADC). To prevent errors in the conversion process, Anti-Aliasing Filters are employed prior to the ADC Digital filtering is performed after the ADC using a Digital Signal Processor, which is implemented on the QF4A512.

  • 9.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Data acquisition and digital filtering for infrasonic records on active volcanoes2007In: Sensors & Transducers Journal, ISSN 2306-8515, E-ISSN 1726-5479, ISSN 1726-5479, Vol. 77, no 3, p. 1058-1064Article in journal (Refereed)
    Abstract [en]

    This paper presents the design of a digital data acquisition system for volcanic infrasound records. The system includes four electret condenser element microphones, a QF4A512 programmable signal converter from Quickfilter Technologies and a MSP430 microcontroller from Texas Instruments. The signal output of every microphone is converted to digital via a 16-bit Analog to Digital Converter (ADC). To prevent errors in the conversion process, Anti-Aliasing Filters are employed prior to the ADC. Digital filtering is performed after the ADC using a Digital Signal Processor, which is implemented on the QF4A512. The four digital signals are summed to get only one signal. Data storing and digital wireless data transmission will be described in a future paper.

  • 10.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Hardware implementation of 1D wavelet transform on an FPGA for infrasound signal classification2008In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 55, no 1, p. 9-13Article in journal (Refereed)
    Abstract [en]

    Infrasound is a low frequency acoustic phenomenon that typically ranges from 0.01 to 20 Hz. The data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, i.e., the Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important for real-time signal classification. Performing wavelet decomposition on the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of discrete wavelet transforms (DWT) for real-time infrasound data processing; our approach uses only two FIR filters, a high-pass and a low-pass filter. A compact implementation was realized with pipelining techniques and multiple use of generalized building blocks. The design was described in VHDL and the FPGA implementation and simulation were performed on the QUARTUS II platform.

  • 11.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Real-time signal processing of infrasound data using 1D wavelet transform on FPGA device2007In: 2007 15th IEEE-NPSS Real-Time Conference, Vols 1 And 2, 2007, p. 170-174Conference paper (Refereed)
    Abstract [en]

    Infrasound is a low frequency acoustic phenomenon typically in the frequency range 0.01 to 20 Hz. Data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important. Using wavelet decomposition as a tool for removing noise from the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of Discrete Wavelet Transform (DWT) for real-time infrasound data processing, in which only two FIR filters, a high-pass and a low-pass filter, are used. With the filter reuse method and techniques such as pipeline, basic operations, by the VHDL on the platform QUARTUS II, FPGA simulation and implementation are fulfilled. This implementation takes advantage from the low sampling rate used by the infrasound monitoring system that is only 18 Hz.

  • 12.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Olsson, Roland
    Ostfold University College, Faculty of Computer Science.
    Hansen, Stig-Erland
    Ostfold University College, Faculty of Computer Science.
    Comparison of three feature extraction techniques to distinguish between different infrasound signals2007In: Progress in Pattern Recognition / [ed] Singh S; Singh M, 2007, p. 75-82Conference paper (Refereed)
    Abstract [en]

    The main aim of this paper is to compare three feature extraction techniques, Discrete Wavelet Transform, Time Scale Spectrum using Continuous Wavelet Transforms, and Cepstral Coefficients and their derivatives, for the purposes of classifying time series type ;signal data. The features are classified by two types of neural networks. The paper draws a number of important conclusions on the suitability of these features for analysis, and provides a good comparative evaluation on four different data sets.

  • 13.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Olsson, Roland
    Ostfold University College.
    Hansen, Stig-Erland
    Ostfold University College.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Classification of infrasound events with various machine learning techniques2007In: CITSA 2007 - Int. Conference on Cybernetics and Information Technologies, Systems and Applications and CCCT 2007 - Int. Conference on Computing, Communications and Control Technologies, Proceedings: Vol II / [ed] Savoie M; Aguilar J; Chu HW; Zinn CD; AroshaS SMN, International Institute of Informatics and Systemics, 2007, p. 191-195Conference paper (Refereed)
    Abstract [en]

    This paper presents classification results for infrasonic events using practically all well-known machine learning algorithms together with wavelet transforms for preprocessing. We show that there are great differences between different groups of classification algorithms and that nearest neighbor classifiers are superior to all others for accurate classification of infrasonic events.

  • 14. Horyath, Gyorgy
    et al.
    Chilo, Jose
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Different volatile signals emitted by human ovarian carcinoma and healthy tissue2010In: FUTURE ONCOL, ISSN 1479-6694, Vol. 6, no 6, p. 1043-1049Article in journal (Refereed)
    Abstract [en]

    Many cancers are detected at a late stage resulting in high mortality rates. Thus, it is essential to develop inexpensive and simple methods for early diagnosis. Detection of different malignancies using canine scent, as well as other technical methods, has been reported in peer-reviewed journals, indicating that this may represent a new diagnostic tool for malignancies. Aim: This study aims to test the detection of different volatile organic compound signals emitted by ovarian carcinoma and normal tissues. Materials & methods: A previously tested electronic nose is used in the pilot study to analyze human grade 3 seropapillary ovarian carcinoma samples. The recorded signals were compared with healthy human Fallopian tube specimens. A variety of algorithms were tested and confusion matrices compared. In parallel, an external validation study was performed using the same type and grade of human ovarian carcinomas with healthy myometrium (first part) and postmenopausal ovarium (second part) specimens as controls. Both sample types were obtained from individuals who did not participate in the pilot study. Results: Method sensitivity was 100% (15 of 15) in the pilot study. The first part of the validation study demonstrated that 84.8% of cancer tissues (sensitivity: 84.8%) and 88.6% of the control samples (specificity: 88.6%) were correctly classified. In the second part the JRip algorithm correctly classified 75% of cancer tissues (sensitivity: 75%) and 80% of the control ovarian tissues (specificity: 80%). Collating results gives a sensitivity of 84,4%, whereas overall specificity was 86.8%. Conclusion: Although based on a limited number of samples, our results strongly suggest that specific volatile organic compound signals emitted by ovarian carcinomas may be used for early diagnosis of the disease.

  • 15. Kermit, M.
    et al.
    Eide, A. J.
    Lindblad, Thomas
    KTH, Superseded Departments, Physics.
    Waldemark, K.
    Treatment of obstructive sleep apnea syndrome by monitoring patients airflow signals2000In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 21, no 3, p. 277-281Article in journal (Refereed)
    Abstract [en]

    The breathing patterns from sleeping persons suffering from sleep apnea have been measured. A method based on the neural network-like O-algorithm has been applied to capture the onset of sleep apnea. This method is suggested as an indicator for early on-line detection of obstructions in the upper airway. Results from the system tested with airflow signals recorded from five patients during sleep indicate acceptable performance and treatment for developing apnea is possible.

  • 16. Kinser, J. M.
    et al.
    Waldemark, K.
    Lindblad, Thomas
    KTH, Superseded Departments, Physics.
    Jacobsson, S. P.
    Multidimensional pulse image processing of chemical structure data2000In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 51, no 1, p. 115-124Article in journal (Refereed)
    Abstract [en]

    Recent advances in the understanding of the mammalian visual cortex have led to new approaches for image processing techniques. As a result of this, computer simulations using the proposed visual cortex model have become very useful in the field of image processing. Models of this kind have the ability to efficiently extract image segments, edges and texture. They operate by generating a set of pulse images (images with binary pixels) for a static input. These pulse images display synchronized activity of neighboring neurons, and it is these images in which the information about segments, edges and texture are displayed. Pulse image generation is dependent on autowaves that travel throughout the image. In order to extend pulse image processing to multidimensional data (i.e., data cubes), the autowaves are designed to expand in all of the cube's dimensions. In this fashion, pulse cubes can be created and the same analysis techniques that have been applied to two-dimensional pulse images can be applied to pulse image cubes. This paper examines and discusses multidimensional pulse image analysis applied to three-dimensional (3D) chemical structural data of 17 beta-estradiol.

  • 17.
    Lindblad, Thomas
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Kinser, J. M.
    Image processing using pulse-coupled neural networks2005Book (Refereed)
    Abstract [en]

    Thomas Lindblad is a professor at the Royal Institute of Technology (Physics) in Stockholm. Working and teaching nuclear and environmental physics his main interest is with sensors, signal processing and intelligent data analysis of torrent data from experiments on-line accelerators, in space, etc. Jason Kinser is an associate professor at George Mason University. He has developed a plethora of image processing applications in the medical, military, and industrial fields. He has been responsible for the conversion of PCNN theory into practical applications providing many improvements in both speed and performance.

  • 18. Waldemark, J.
    et al.
    Millberg, M.
    Lindblad, Thomas
    KTH, Superseded Departments, Physics.
    Waldemark, K.
    Image analysis for airborne reconnaissance and missile applications2000In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 21, no 3, p. 239-251Article in journal (Refereed)
    Abstract [en]

    This paper describes how the pulse coupled neural network (PCNN) can be used in various image analysis applications. We especially focus on two time-critical applications, in particular, airborne reconnaissance and missile navigation. Today, biologically inspired sensor analysis systems such as the PCNN can be used in many different applications related to these two major applications. New ideas are shown on how to use PCNN in combination with other image processing transforms, e.g. the Radon transform and foveation point detection to solve image interpretation and missile navigation problems. This includes solving tasks such as image segmentation, object detection and target identification. Finally, a VHDL implementation of the PCNN targeting FPGA is presented.

  • 19. Waldemark, K.
    et al.
    Lindblad, Thomas
    KTH, Superseded Departments, Physics.
    Becanovic, V.
    Guillen, J. L. L.
    Klingner, P. L.
    Patterns from the sky - Satellite image analysis using pulse coupled neural networks for pre-processing, segmentation and edge detection2000In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 21, no 3, p. 227-237Article in journal (Refereed)
    Abstract [en]

    In this work we attempt to distinguish land from water in satellite images, specifically images taken by the FORTE satellite. First, we successfully approximate areas hidden by stationary artefacts in the image. We then segment regions of land from water. Finally, we determine the boundaries of the surrounding landmasses.

  • 20.
    Zetterlund, Nils
    et al.
    KTH, Superseded Departments, Physics.
    Lindblad, Thomas
    KTH, Superseded Departments, Physics.
    Ekblad, Ulf
    KTH, Superseded Departments, Physics.
    The minimum risk angle for automatic target recognition using the intersecting cortical model2004In: Proc. Seventh Int. Conf. Inf. Fusion, 2004, p. 1014-1021Conference paper (Refereed)
    Abstract [en]

    While kids easily find 3-D objects like animals in a scene (e.g. a photograph), this is still not the case for algorithms running on von Neumann computers or neural network chips. The present investigation has two goals: Finding "signatures" of the object in the scene and trying to find out at which observation angle the chance of correct identification is the best. By signatures we mean a vector of reasonable size (say 50 elements). Clearly a cow looks different from the back or from the side. A car is probably more easily identified viewed from the front than it is from above. For a plane it may be the other way around. Thus if we define a general but compact "signature" of the object, it will surely depend on the viewing angle. The problem of finding the most optimal viewing angle is dealt with in this paper.

1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf