| 1. |
- A. Mouris, Boules, et al.
(author)
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Multi-tone Signal Optimization for Wireless Power Transfer in the Presence of Wireless Communication Links
- 2020
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In: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 19:5, s. 3575-3590
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Journal article (peer-reviewed)abstract
- In this paper, we study optimization of multi-tone signals for wireless power transfer (WPT) systems. We investigate different non-linear energy harvesting models. Two of them are adopted to optimize the multi-tone signal according to the channel state information available at the transmitter. We show that a second-order polynomial curve-fitting model can be utilized to optimize the multi-tone signal for any RF energy harvester design. We consider both single-antenna and multi-antenna WPT systems. In-band co-existing communication links are also considered in this work by imposing a constraint on the received power at the nearby information receiver to prevent its RF front end from saturation. We emphasize the importance of imposing such constraint by explaining how inter-modulation products, due to saturation, can cause high interference at the information receiver in the case of multi-tone signals. The multi-tone optimization problem is formulated as a non-convex linearly constrained quadratic program. Two globally optimal solution approaches using mixed-integer linear programming and finite branch-and-bound techniques are proposed to solve the problem. The achieved improvement resulting from applying both solution methods to the multi-tone optimization problem is highlighted through simulations and comparisons with other solutions existing in the literature.
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| 2. |
- Aad, G, et al.
(author)
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Performance of the upgraded PreProcessor of the ATLAS Level-1 Calorimeter Trigger
- 2020
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In: Journal of Instrumentation. - : IOP Publishing. - 1748-0221. ; 15:11
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Journal article (peer-reviewed)abstract
- The PreProcessor of the ATLAS Level-1 Calorimeter Trigger prepares the analogue trigger signals sent from the ATLAS calorimeters by digitising, synchronising, and calibrating them to reconstruct transverse energy deposits, which are then used in further processing to identify event features. During the first long shutdown of the LHC from 2013 to 2014, the central components of the PreProcessor, the Multichip Modules, were replaced by upgraded versions that feature modern ADC and FPGA technology to ensure optimal performance in the high pile-up environment of LHC Run 2. This paper describes the features of the new Multichip Modules along with the improvements to the signal processing achieved. c 2020 CERN for the benefit of the ATLAS collaboration. Published by IOP Publishing Ltd on behalf of Sissa Medialab.
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| 3. |
- Aarestrup, FM, et al.
(author)
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Towards a European health research and innovation cloud (HRIC)
- 2020
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In: Genome medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 12:1, s. 18-
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Journal article (peer-reviewed)abstract
- The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.
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| 4. |
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| 5. |
- Abascal, Angela, et al.
(author)
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Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas
- 2022
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In: Computers, Environment and Urban Systems. - : Elsevier BV. - 0198-9715 .- 1873-7587. ; 95
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Journal article (peer-reviewed)abstract
- Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituting an urgent knowledge gap. The objective of this study is to apply deep learning techniques and morphological analysis to identify degrees of deprivation in DUAs. To this end, we first generate a reference dataset of building footprints using a participatory community-based crowd-sourcing approach. Secondly, we adapt a deep learning model based on the U-Net architecture for the semantic segmentation of satellite imagery (WorldView 3) to generate building footprints. Lastly, we compute multi-level morphological features from building footprints for identifying the deprivation variation within DUAs. Our results show that deep learning techniques perform satisfactorily for predicting building footprints in DUAs, yielding an accuracy of F1 score = 0.84 and Jaccard Index = 0.73. The resulting building footprints (predicted buildings) are useful for the computation of morphology metrics at the grid cell level, as, in high-density areas, buildings cannot be detected individually but in clumps. Morphological features capture physical differences of deprivation within DUAs. Four indicators are used to define the morphology in DUAs, i.e., two related to building form (building size and inner irregularity) and two covering the form of open spaces (proximity and directionality). The degree of deprivation can be evaluated from the analysis of morphological features extracted from the predicted buildings, resulting in three categories: high, medium, and low deprivation. The outcome of this study contributes to the advancement of methods for producing up-to-date and disaggregated morphological spatial data on urban DUAs (often referred to as 'slums') which are essential for understanding the physical dimensions of deprivation, and hence planning targeted interventions accordingly.
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| 6. |
- Abbas, Zainab
(author)
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Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Recent years have witnessed a massive increase in the amount of data generated by the Internet of Things (IoT) and social media. Processing huge amounts of this data poses non-trivial challenges in terms of the hardware and performance requirements of modern-day applications. The data we are dealing with today is of massive scale, high intensity and comes in various forms. MapReduce was a popular and clever choice of handling big data using a distributed programming model, which made the processing of huge volumes of data possible using clusters of commodity machines. However, MapReduce was not a good fit for performing complex tasks, such as graph processing, iterative programs and machine learning. Modern data processing frameworks, that are being popularly used to process complex data and perform complex analysis tasks, overcome the shortcomings of MapReduce. Some of these popular frameworks include Apache Spark for batch and stream processing, Apache Flink for stream processing and Tensor Flow for machine learning.In this thesis, we deal with complex analytics on data modeled as time series, graphs and streams. Time series are commonly used to represent temporal data generated by IoT sensors. Analysing and forecasting time series, i.e. extracting useful characteristics and statistics of data and predicting data, is useful for many fields that include, neuro-physiology, economics, environmental studies, transportation, etc. Another useful data representation we work with, are graphs. Graphs are complex data structures used to represent relational data in the form of vertices and edges. Graphs are present in various application domains, such as recommendation systems, road traffic analytics, web analysis, social media analysis. Due to the increasing size of graph data, a single machine is often not sufficient to process the complete graph. Therefore, the computation, as well as the data, must be distributed. Graph partitioning, the process of dividing graphs into subgraphs, is an essential step in distributed graph processing of large scale graphs because it enables parallel and distributed processing.The majority of data generated from IoT and social media originates as a continuous stream, such as series of events from a social media network, time series generated from sensors, financial transactions, etc. The stream processing paradigm refers to the processing of data streaming that is continuous and possibly unbounded. Combining both graphs and streams leads to an interesting and rather challenging domain of streaming graph analytics. Graph streams refer to data that is modelled as a stream of edges or vertices with adjacency lists representing relations between entities of continuously evolving data generated by a single or multiple data sources. Streaming graph analytics is an emerging research field with great potential due to its capabilities of processing large graph streams with limited amounts of memory and low latency. In this dissertation, we present graph partitioning techniques for scalable streaming graph and time series analysis. First, we present and evaluate the use of data partitioning to enable data parallelism in order to address the challenge of scale in large spatial time series forecasting. We propose a graph partitioning technique for large scale spatial time series forecasting of road traffic as a use-case. Our experimental results on traffic density prediction for real-world sensor dataset using Long Short-Term Memory Neural Networks show that the partitioning-based models take 12x lower training time when run in parallel compared to the unpartitioned model of the entire road infrastructure. Furthermore, the partitioning-based models have 2x lower prediction error (RMSE) compared to the entire road model. Second, we showcase the practical usefulness of streaming graph analytics for large spatial time series analysis with the real-world task of traffic jam detection and reduction. We propose to apply streaming graph analytics by performing useful analytics on traffic data stream at scale with high throughput and low latency. Third, we study, evaluate, and compare the existing state-of-the-art streaming graph partitioning algorithms. We propose a uniform analysis framework built using Apache Flink to evaluate and compare partitioning features and characteristics of streaming graph partitioning methods. Finally, we present GCNSplit, a novel ML-driven streaming graph partitioning solution, that uses a small and constant in-memory state (bounded state) to partition (possibly unbounded) graph streams. Our results demonstrate that \ours provides high-throughput partitioning and can leverage data parallelism to sustain input rates of 100K edges/s. GCNSplit exhibits a partitioning quality, in terms of graph cuts and load balance, that matches that of the state-of-the-art HDRF (High Degree Replicated First) algorithm while storing three orders of magnitude smaller partitioning state.
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| 7. |
- Abbasi-Ravasjani, Sonia, et al.
(author)
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Sulfated carboxymethyl cellulose and carboxymethyl kappa-carrageenan immobilization on 3D-printed poly-epsilon-caprolactone scaffolds differentially promote pre-osteoblast proliferation and osteogenic activity
- 2022
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In: Frontiers in Bioengineering and Biotechnology. - : Frontiers Media SA. - 2296-4185. ; 10
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Journal article (peer-reviewed)abstract
- The lack of bioactivity in three-dimensional (3D)-printing of poly-epsilon-caprolactone (PCL) scaffolds limits cell-material interactions in bone tissue engineering. This constraint can be overcome by surface-functionalization using glycosaminoglycan-like anionic polysaccharides, e.g., carboxymethyl cellulose (CMC), a plant-based carboxymethylated, unsulfated polysaccharide, and kappa-carrageenan, a seaweed-derived sulfated, non-carboxymethylated polysaccharide. The sulfation of CMC and carboxymethylation of kappa-carrageenan critically improve their bioactivity. However, whether sulfated carboxymethyl cellulose (SCMC) and carboxymethyl kappa-carrageenan (CM-kappa-Car) affect the osteogenic differentiation potential of pre-osteoblasts on 3D-scaffolds is still unknown. Here, we aimed to assess the effects of surface-functionalization by SCMC or CM-kappa-Car on the physicochemical and mechanical properties of 3D-printed PCL scaffolds, as well as the osteogenic response of pre-osteoblasts. MC3T3-E1 pre-osteoblasts were seeded on 3D-printed PCL scaffolds that were functionalized by CM-kappa-Car (PCL/CM-kappa-Car) or SCMC (PCL/SCMC), cultured up to 28 days. The scaffolds' physicochemical and mechanical properties and pre-osteoblast function were assessed experimentally and by finite element (FE) modeling. We found that the surface-functionalization by SCMC and CM-kappa-Car did not change the scaffold geometry and structure but decreased the elastic modulus. Furthermore, the scaffold surface roughness and hardness increased and the scaffold became more hydrophilic. The FE modeling results implied resilience up to 2% compression strain, which was below the yield stress for all scaffolds. Surface-functionalization by SCMC decreased Runx2 and Dmp1 expression, while surface-functionalization by CM-kappa-Car increased Cox2 expression at day 1. Surface-functionalization by SCMC most strongly enhanced pre-osteoblast proliferation and collagen production, while CM-kappa-Car most significantly increased alkaline phosphatase activity and mineralization after 28 days. In conclusion, surface-functionalization by SCMC or CM-kappa-Car of 3D-printed PCL-scaffolds enhanced pre-osteoblast proliferation and osteogenic activity, likely due to increased surface roughness and hydrophilicity. Surface-functionalization by SCMC most strongly enhanced cell proliferation, while CM-kappa-Car most significantly promoted osteogenic activity, suggesting that surface-functionalization by CM-kappa-Car may be more promising, especially in the short-term, for in vivo bone formation.
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| 8. |
- Abbasiasl, Taher, et al.
(author)
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A Flexible Cystoscope Based on Hydrodynamic Cavitation for Tumor Tissue Ablation
- 2022
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In: IEEE Transactions on Biomedical Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9294 .- 1558-2531. ; 69:1, s. 513-524
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Journal article (peer-reviewed)abstract
- Objective: Hydrodynamic cavitation is characterized by the formation of bubbles inside a flow due to local reduction of pressure below the saturation vapor pressure. The resulting growth and violent collapse of bubbles lead to a huge amount of released energy. This energy can be implemented in different fields such as heat transfer enhancement, wastewater treatment and chemical reactions. In this study, a cystoscope based on small scale hydrodynamic cavitation was designed and fabricated to exploit the destructive energy of cavitation bubbles for treatment of tumor tissues. The developed device is equipped with a control system, which regulates the movement of the cystoscope in different directions. According to our experiments, the fabricated cystoscope was able to locate the target and expose cavitating flow to the target continuously and accurately. The designed cavitation probe embedded into the cystoscope caused a significant damage to prostate cancer and bladder cancer tissues within less than 15 minutes. The results of our experiments showed that the cavitation probe could be easily coupled with endoscopic devices because of its small diameter. We successfully integrated a biomedical camera, a suction tube, tendon cables, and the cavitation probe into a 6.7 mm diameter cystoscope, which could be controlled smoothly and accurately via a control system. The developed device is considered as a mechanical ablation therapy, can be a solid alternative for minimally invasive tissue ablation methods such as radiofrequency (RF) and laser ablation, and could have lower side effects compared to ultrasound therapy and cryoablation.
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| 9. |
- Abbasiasl, Taher, et al.
(author)
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Effect of intensified cavitation using poly (vinyl alcohol) microbubbles on spray atomization characteristics in microscale
- 2020
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In: AIP Advances. - : American Institute of Physics (AIP). - 2158-3226. ; 10:2
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Journal article (peer-reviewed)abstract
- In this study, cavitating flows inside a transparent cylindrical nozzle with an inner diameter of 0.9 mm were visualized, and the effect of cavitation on atomization characteristics of emerging sprays was investigated. Different patterns of cavitating flows inside the nozzle were visualized using a high-speed camera. In-house codes were developed to process the captured images to study the droplet size distribution and droplet velocity in different flow regimes. The results show that cavitating flows at the microscale have significant effects on atomization characteristics of the spray. Two working fluids, namely, water and poly(vinyl alcohol) microbubble (PVA MB) suspension, were employed. Accordingly, the injection pressures were detected as 690 kPa, 1035 kPa, and 1725 kPa for cavitation inception, supercavitation, and hydraulic flip flow regimes in the case of water, respectively. The corresponding pressures for the aforementioned patterns for PVA MB suspension were 590 kPa, 760 kPa, and 1070 kPa, respectively. At the microscale, as a result of a higher volume fraction of cavitation bubbles inside the nozzle, there is no large difference between the cavitation numbers corresponding to cavitating and hydraulic flip flows. Although the percentage of droplets with diameters smaller than 200 μm was roughly the same for both cases of water and PVA MB suspension, the Sauter mean diameter was considerably lower in the case of PVA MBs. Moreover, higher droplet velocities were achieved in the case of PVA MBs at lower injection pressures.
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| 10. |
- Abbasiverki, Roghayeh, et al.
(author)
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Nonlinear Behaviour of Concrete Buttress Dams under High-Frequency Excitations Taking into Account Topographical Amplifications
- 2021
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In: Shock and Vibration. - : Hindawi Limited. - 1070-9622 .- 1875-9203. ; 2021, s. 1-22
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Journal article (peer-reviewed)abstract
- Concrete buttress dams could potentially be susceptible to high-frequency vibrations, especially in the cross-stream direction, due to their slender design. Previous studies have mainly focused on low-frequency vibrations in stream direction using a simplified foundation model with the massless method, which does not consider topographic amplifications. This paper therefore investigates the nonlinear behaviour of concrete buttress dams subjected to high-frequency excitations, considering cross-stream vibrations. For comparison, the effect of low-frequency excitations is also investigated. The influence of the irregular topography of the foundation surface on the amplification of seismic waves at the foundation surface and thus in the dam is considered by a rigorous method based on the domain-reduction method using the direct finite element method. The sensitivity of the calculated response of the dam to the free-field modelling approach is investigated by comparing the result with analyses using an analytical method based on one-dimensional wave propagation theory and a massless approach. Available deconvolution software is based on the one-dimensional shear wave propagation to transform the earthquake motion from the foundation surface to the corresponding input motion at depth. Here, a new deconvolution method for both shear and pressure wave propagation is developed based on an iterative time-domain procedure using a one-dimensional finite element column. The examples presented showed that topographic amplifications of high-frequency excitations have a significant impact on the response of this type of dam. Cross-stream vibrations reduced the safety of the dam due to the opening of the joints and the increasing stresses. The foundation modelling approach had a significant impact on the calculated response of the dam. The massless method produced unreliable results, especially for high-frequency excitations. The free-field modelling with the analytical method led to unreliable joint openings. It is therefore recommended to use an accurate approach for foundation modelling, especially in cases where nonlinearity is considered.
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