Johan Swärd

• PhD
• PostDoc Position at Lund University

In this short paper, we describe an efficient numerical solver for the optimal sampling problem considered in "Designing Sampling Schemes for Multi-Dimensional Data". An implementation may be found on https://www.maths.lu.se/staff/andreas-jakobsson/publications/.

Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop...

Deep learning based classification for standardization of prostate cancer RT structure annotations C JAMTHEIM GUSTAFSSON*1,2, M LEMPART1,2, J SWÄRD3, E PERSSON1,2, J SCHERMAN1 1 Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund Sweden 2 Department of Medical Physics, Lund University, Malmö, Sweden 3 Centre...

The decomposition of nonlinear chirp modes is a challenging task, typically requiring prior knowledge of the number of modes a signal contains. In this work, we present a greedy nonlinear chirp mode estimation (NCME) technique that forms the used decomposition basis from the signal itself, using an arctangent demodulation technique. The resulting d...

Identification of prostate gold fiducial markers in MRI images is challenging when CT images are not available, due to misclassifications from intra-prostatic calcifications. It is also a time consuming task and automated identification methods have been suggested as an improvement for both objectives. Multi-echo gradient echo (MEGRE) images have b...

In this paper, we propose a blind channel deconvolution method based on a sparse reconstruction framework exploiting a wideband dictionary under the (relatively weak) assumption that the transmitted signal may be assumed to be well modelled as a sum of sinusoids. Using a Toeplitz structured formulation of the received signal, we form an iterative b...

In this work, we consider the problem of estimating the parameters of polynomially damped sinusoidal signals, commonly encountered in, for instance, spectroscopy. Generally, finding the parameter values of such signals constitutes a high-dimensional problem, often further complicated by not knowing the number of signal components or their specific...

Non-uniform sampling (NUS) of multi-dimensional NMR data offers significant time savings while improving spectral resolution or increasing sensitivity per unit time. However, NUS has not been widely used for quantitative analysis due to the non-linearity of most methods used to model NUS data, which leads to problems in estimating signal intensitie...

In this paper, we examine the problem of target detection for multistatic passive radar. Passive radar systems leverage existing wireless sources, such as radio/TV stations and cellular signals which are referred to as illuminators of opportunity (IOs), to illuminate the environment and provide surveillance functions. Usually, these IO source signa...

BACKGROUND AND AIM Non-stop stair ascending at maximum speed is required to reach a safe refuge level from deep underground structures, such as subways and in high-rise buildings in an emergency evacuation situation. Endurance of stair climbing and identifying the time of the onset of leg’s local muscle fatigue (LMF) are interests in evacuation res...

In this paper, we propose a gridless method for estimating an unknown number of fundamental frequencies. Starting with a conventional dictionary matrix, containing sets of candidate fundamental frequencies and their corresponding harmonics, a non-convex log-sum cost function is formed such that it imposes the harmonic structure and treats every fun...

This work proposes a multi-dimensional frequency and amplitude estimator tailored for noise corrupted signals that have been clipped. Formulated as a sparse reconstruction problem, the proposed algorithm estimates the signal parameters by solving an atomic norm minimization problem. The estimator also exploits the waveform information provided by t...

In this work, we generalize the recent sparse iterative covariance-based estimator (SPICE) by extending the problem formulation to allow for different norm constraints on the signal and noise parameters in the covariance model. The resulting extended SPICE algorithm offers the same benefits as the regular SPICE algorithm, including being hyper-para...

In this work, we propose a method for determining a non-uniform sampling scheme for multi-dimensional signals by solving a convex optimization problem reminiscent of the sensor selection problem. The resulting sampling scheme minimizes the sum of the Cram\'er-Rao lower bound for the parameters of interest, given a desired number of sampling points....

In this work, we propose a method for determining a non-uniform sampling scheme for multi-dimensional signals by solving a convex optimization problem reminiscent of the sensor selection problem. The resulting sampling scheme minimizes the sum of the Cram\'er-Rao lower bound for the parameters of interest, given a desired number of sampling points....

In this paper, we introduce a wideband dictionary framework for estimating sparse signals. By formulating integrated dictionary elements spanning bands of the considered parameter space, one may efficiently find and discard large parts of the parameter space not active in the signal. After each iteration, the zero-valued parts of the dictionary may...

In this paper, we introduce a wideband dictionary framework for estimating sparse signals. By formulating integrated dictionary elements spanning bands of the considered parameter space, one may efficiently find and discard large parts of the parameter space not active in the signal. After each iteration, the zero-valued parts of the dictionary may...

In this work, we propose a time-recursive multipitch estimation algorithm using a sparse reconstruction framework, assuming that only a few pitches from a large set of candidates are active at each time instant. The proposed algorithm does not require any training data, and instead utilizes a sparse recursive least squares formulation augmented by...

Collaborative filtering for recommender systems seeks to learn and predict user preferences for a collection of items by identifying similarities between users on the basis of their past interest or interaction with the items in question. In this work, we present a conjugate prior regularized extension of Hofmann's Gaussian emission probabilistic l...

This work treats the estimation of chroma features for harmonic audio signals using a sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a periodogram-based approach that maps the fundamental frequency of a musical tone to its corresponding chroma. Such an approach often...

In this work, we consider the problem of high-resolution estimation of the parameters detailing an N-dimensional (N-D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Since such signals are not sparse in an oversampled Fourier matrix, earlier approaches typically exploit large dictionary matrices that include...

In this work, we extend on our recently proposed block sparse chroma estimator, such that the method also allows for signals with time-varying envelopes. Using a spline-based amplitude modulation of the chroma dictionary, the refined estimator is able to model longer frames than our earlier approach, as well as to model highly time-localized signal...

In this work, we present a method for estimating the parameters detailing an unknown number of linear chirp signals, using an iterative sparse reconstruction framework. The proposed method is initiated by a re-weighted Lasso approach, and then use an iterative relaxation-based refining step to allow for high resolution estimates. The resulting esti...

In this paper, we propose a method for estimating statistical periodicities in symbolic sequences. Different from other common approaches used for the estimation of periodicities of sequences of arbitrary, finite, symbol sets, that often map the symbolic sequence to a numerical representation, we here exploit a likelihood-based formulation in a spa...

In this paper, we present a method for estimating the parameters detailing an unknown number of linear, possibly harmonically related, chirp signals, using an iterative sparse reconstruction framework. The proposed method is initiated by a re-weighted group-sparsity approach, followed by an iterative relaxation-based refining step, to allow for hig...

In this paper, we propose a novel interference cancellation method that exploits secondary data to estimate stationary interference components present in both the primary and the secondary data sets, thereby allowing for the removal of such interference from the data sets, even when these components share frequencies with the signal of interest. Th...

In this paper, we introduce a time-frequency spectral estimator for smooth spectra, allowing for irregularly sampled measurements. A non-parametric representation of the time dependent (TD) covariance matrix is formed by assuming that the spectrum is piecewise linear. Using this representation, the time-frequency spectrum is then estimated by solvi...

We consider the problem of sparse modeling of a signal consisting of an unknown number of exponentially decaying sinusoids. Since such signals are not sparse in an oversampled Fourier matrix, earlier approaches typically exploit large dictionary matrices that include not only a finely spaced frequency grid but also a grid over the considered dampin...

In this work, we propose a novel subspace-based estimator of periodicities in symbolic sequences. The estimator exploits the harmonic structure naturally occurring in symbolic sequences and iteratively forms the estimate of the periodicities using a MUSIC-like formulation. The estimator allows for alphabets of different sizes, but is here illustrat...

This paper proposes a novel non-parametric estimator for spectroscopic echo-train signals, termed ETCAPA, to be used as a robust and reliable first-approach-technique for new, unknown, or partly disturbed substances. Exploiting the complete echo structure for the signal of interest, the method reliably estimates all parameters of interest, enabling...

In this work, we propose a method of estimating periodicities in symbolic sequences, allowing for arbitrary, finite, symbol sets. Different from other common approaches, that often map the symbolic sequence to a numerical representation, we here exploit a likelihood-based formulation to represent the periodic behavior of the sequence. The performan...