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  • 1.
    del Aguila Pla, Pol
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Saxena, Vidit
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Jaldén, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    SpotNet – Learned iterations for cell detection in image-based immunoassays2019Conference paper (Refereed)
    Abstract [en]

    Accurate cell detection and counting in the image-based ELISpot and FluoroSpot immunoassays is a challenging task. Recently proposed methodology matches human accuracy by leveraging knowledge of the underlying physical process of these assays and using proximal optimization methods to solve an inverse problem. Nonetheless, thousands of computationally expensive iterations are often needed to reach a near-optimal solution. In this paper, we exploit the structure of the iterations to design a parameterized computation graph, SpotNet, that learns the patterns embedded within several training images and their respective cell information. Further, we compare SpotNet to a convolutional neural network layout customized for cell detection. We show empirical evidence that, while both designs obtain a detection performance on synthetic data far beyond that of a human expert, SpotNet is easier to train and obtains better estimates of particle secretion for each cell.

  • 2.
    Saxena, Vidit
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. Ericsson Res, Stockholm, Sweden..
    Cavarec, Baptiste
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Jaldén, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Bengtsson, Mats
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Tullberg, Hugo
    Ericsson Res, Stockholm, Sweden..
    A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems2018In: 2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS / [ed] Matthews, M B, IEEE , 2018, p. 1800-1804Conference paper (Refereed)
    Abstract [en]

    For spatial modulation (SNI) systems that utilize multiple transmit antennas/patterns with a single radio front-end, we propose a learning approach to predict the average symbol error rate (SER) conditioned on the instantaneous channel state. We show that the predicted SER can he used to lower the average SER over Rayleigh fading channels by selecting the optimal codebook in each transmission instance. Further by exploiting that feedforward artificial neural networks (ANNs) trained with a mean squared error (MSE) criterion estimate the conditional a posteriori probabilities, we maximize the expected rate for each transmission instance and thereby improve the link spectral efficiency.

  • 3.
    Saxena, Vidit
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden.;Ericsson Res, Stockholm, Sweden..
    Jaldén, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden..
    Bengtsson, Mats
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden..
    Tullberg, Hugo
    Ericsson Res, Stockholm, Sweden..
    DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 6658-6662Conference paper (Refereed)
    Abstract [en]

    In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution. Further, we exploit deep neural networks as a general purpose tool to implement our model and propose a training scheme for which, even while training with the binary frame error events (i.e., ACKs/NACKs), the network outputs converge to the FEP conditioned on the input channel state. We provide simulation results that demonstrate gains in the FEP prediction accuracy with our approach as compared to the traditional effective exponential SIR metric (EESM) approach for a range of channel code rates, and show that these gains can be exploited to increase the link throughput.

  • 4. Saxena, Vidit V.
    et al.
    Feldt, Tommy
    KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID.
    Goel, M.
    Augmented telepresence as a tool for immersive simulated dancing in experience and learning2014In: ACM International Conference Proceeding Series, ACM Digital Library, 2014, p. 86-89Conference paper (Refereed)
    Abstract [en]

    The paper explores the use of interaction technologies in the domain of dance and attempts to visualize a future tool to complement current applications. It begins with a review of various tools and technologies that have been used within the domain in the past and make a projection for how interaction technologies could develop in the coming decade. It then presents a conceptual tool for simulated dancing - 'disDans', which utilizes the modalities of touch, vision and hearing in order to provide an immersive experience. It allows multiple users to touch and feel each other while dancing together, without having to be physically present in the same space. In the end the paper discusses some challenges and limitations to the proposal.

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