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Due to its range-dependent target response, frequency diverse array multiple-input multiple-output (FDA-MIMO) radar systems enable superior detection capabilities as compared with conventional phased-array radars. This paper proposes an incoherent detector for airborne FDA-MIMO radar to detect independent but possibly non-identically distributed fluctuating targets. For FDA-MIMO, variations in the
The European Copernicus programme plans to install a constellation of multiple polar orbiting satellites (Copernicus Anthropogenic CO2 Monitoring Mission, CO2M mission) for observing atmospheric CO2 content with the aim to estimate fossil fuel CO2 emissions. We explore the impact of potential CO2M observations of column-averaged CO2 (XCO2), nitrogen dioxide (NO2), and aerosols in a 200 × 200 km2 d
Given a sensor network, TDOA self-calibration aims at simultaneously estimating the positions of receivers and transmitters, and transmitters time offsets. This can be formulated as a system of polynomial equations. Due to the elevated number of unknowns and the nonlinearity of the problem, obtaining an accurate solution efficiently is nontrivial. Previous work has shown that iterative algorithms
Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these use recognition of high level image features to predict reasonably looking depth maps,the result often has poor metric accuracy. Moreover,the standard feed forward architecture does not allow modification of the prediction based on cues other than the image.In this paper we rel
Open-Source Software (OSS) is increasingly common in industry software and enables developers to build better applications, at a higher pace, and with better security. These advantages also come with the cost of including vulnerabilities through these third-party libraries. The largest publicly available database of easily machine-readable vulnerabilities is the National Vulnerability Database (NV
Cardinality and rank functions are ideal ways of regularizing under-determined linear systems, but optimization of the resulting formulations is made difficult since both these penalties are non-convex and discontinuous. The most common remedy is to instead use the ℓ1- and nuclear norms. While these are convex and can therefore be reliably optimized, they suffer from a shrinking bias that degrades
Intensity Analysis (IA) of land use and land cover change (LULCC) is important to support policy and practice. We applied IA to analyse LULCC in Sironko catchment, Uganda. Results show that agricultural land, forest, and wetland reduced by 8%; 32%; and 20% between 1986-2000. Between 2000-2016, forest, wetland, and built-up areas increased by 84%, 5%, and 57%. Active gainers between 1986-2000 were
Globally, solar projects are being rapidly built or planned, particularly in high solar potential regions with high energy demand. However, their energy generation potential is highly related to the weather condition. Here we use state-of-the-art Earth system model simulations to investigate how large photovoltaic solar farms in the Sahara Desert could impact the global cloud cover and solar gener
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet, are usually high-dimensional; 128 dimensions or even more. The higher the dimensionality, the large
In this work, we consider the problem of localizing multiple signal sources based on time-difference of arrival (TDOA) measurements. In the blind setting, in which the source signals are not known, the localization task is challenging due to the data association problem. That is, it is not known which of the TDOA measurements correspond to the same source. Herein, we propose to perform joint local
To train well-performing generalizing neural networks, sufficiently large and diverse datasets are needed. Collecting data while adhering to privacy legislation becomes increasingly difficult and annotating these large datasets is both a resource-heavy and time-consuming task. An approach to overcome these difficulties is to use synthetic data since it is inherently scalable and can be automatical
We consider a perturbation problem for embedded eigenvalues of a self-adjoint differential operator in L2(R;Rn). In particular, we study the set of all small perturbations in an appropriate Banach space for which the embedded eigenvalue remains embedded in the continuous spectrum. We show that this set of small perturbations forms a smooth manifold and we specify its co-dimension. Our methods invo
It is difficult to perform 3D reconstruction from on-vehicle gathered video due to the large forward motion of the vehicle. Even object detection and human sensing models perform significantly worse on onboard videos when compared to standard benchmarks because objects often appear far away from the camera compared to the standard object detection benchmarks, image quality is often decreased by mo
Realistic modelling of pedestrians in Autonomous Vehicles (AV)s and AV testing is crucial to avoid lethal collisions in deployment. The majority of AV trajectory forecasting literature do not utilize the motion cues present in 3D human pose because it is hard to gather large datasets of articulated 3D pedestrian motion. In this thesis we discuss the difficulties in data gathering and propose a ped
In this paper we discuss AI in industrial robotics. In automatic control, computer vision and optimization, ma- chine learning and data mining algorithms are widely used. However, cognition enabling mechanisms, such as high-level logic and symbolic reasoning, are still limited. This is not due to the lack of available algorithms, rather the bottleneck is knowledge representation, acquisition and t
Task-level industrial robot programming is a mundane, error-prone activity requiring expertise and skill. Since humans easily communicate with natural language (NL), it may be attractive to use speech or text as instruction means for robots. However, there has to be a substantial amount of knowledge in the system to translate the high-level language instructions to executable robot programs. In th
Microsoft Word - Neurology Clinic report 2019.docx Neurology clinic 1. Short description of the platform Well-trained and experienced research nurses, research coordinators and research doctors are the backbone of the clinical research activities and have over the years become an essential asset to conduct studies. Currently at the department of neurology there are more than 500 patients enrolled
https://www.multipark.lu.se/sites/multipark.lu.se/files/neurology_clinic_report_2019_.pdf - 2026-05-23
Nepal has made major progress in expanding its national electricity grid, creating the potential for a double transition as increased access to electricity benefits the energy-poor while setting the infrastructural ground for a transition to renewable energy sources. However, despite increased access, many households in Nepalcontinue to rely on traditional and transition fuels such as firewood and
Variabellista för GÅS-baseline med definierade frågeinstrument - rev 20190401 Sölve Elmståhl 2019-04-01/rev 1 Variabelförteckning Gott Åldrande i Skåne (GÅS), basundersökning (2001-2004; n=2931) Projektledare Sölve Elmståhl, Avd. för geriatrik Lunds universitet, Skånes univesitetssjukhus Malmö. Solve.Elmstahl@med.lu.se Sektion A Demografi Civilstånd Antal äktenskap Födelseland Utbildning (antal år
https://www.geriatrik.lu.se/sites/geriatrik.lu.se/files/gas_variabelfortackning_rev2019-04-01_tg.pdf - 2026-05-23
