Articles de revistahttp://hdl.handle.net/2117/1846982024-03-28T21:45:33Z2024-03-28T21:45:33ZENN: a neural network with DCT adaptive activation functionsMartínez Gost, MarcPérez Neira, Ana IsabelLagunas Hernandez, Miguel A.http://hdl.handle.net/2117/4055152024-03-28T12:15:43Z2024-03-28T12:13:49ZENN: a neural network with DCT adaptive activation functions
Martínez Gost, Marc; Pérez Neira, Ana Isabel; Lagunas Hernandez, Miguel A.
The expressiveness of neural networks highly depends on the nature of the activation function, although these are usually assumed predefined and fixed during the training stage. Under a signal processing perspective, in this paper we present Expressive Neural Network (ENN), a novel model in which the non-linear activation functions are modeled using the Discrete Cosine Transform (DCT) and adapted using backpropagation during training. This parametrization keeps the number of trainable parameters low, is appropriate for gradient-based schemes, and adapts to different learning tasks. This is the first non-linear model for activation functions that relies on a signal processing perspective, providing high flexibility and expressiveness to the network. We contribute with insights in the explainability of the network at convergence by recovering the concept of bump, this is, the response of each activation function in the output space. Finally, through exhaustive experiments we show that the model can adapt to classification and regression tasks. The performance of ENN outperforms state of the art benchmarks, providing above a 40% gap in accuracy in some scenarios.
2024-03-28T12:13:49ZMartínez Gost, MarcPérez Neira, Ana IsabelLagunas Hernandez, Miguel A.The expressiveness of neural networks highly depends on the nature of the activation function, although these are usually assumed predefined and fixed during the training stage. Under a signal processing perspective, in this paper we present Expressive Neural Network (ENN), a novel model in which the non-linear activation functions are modeled using the Discrete Cosine Transform (DCT) and adapted using backpropagation during training. This parametrization keeps the number of trainable parameters low, is appropriate for gradient-based schemes, and adapts to different learning tasks. This is the first non-linear model for activation functions that relies on a signal processing perspective, providing high flexibility and expressiveness to the network. We contribute with insights in the explainability of the network at convergence by recovering the concept of bump, this is, the response of each activation function in the output space. Finally, through exhaustive experiments we show that the model can adapt to classification and regression tasks. The performance of ENN outperforms state of the art benchmarks, providing above a 40% gap in accuracy in some scenarios.Land, jet stream, and other atmospheric effects on burned area estimation during the South Asian heatwave of 2022Irawan, Amir MustofaVall-Llossera Ferran, Mercedes MagdalenaLópez Martínez, CarlosCamps Carmona, Adriano JoséChaparro Danon, DavidPortal González, GerardPablos Hernández, MiriamAlonso González, Albertohttp://hdl.handle.net/2117/4034292024-03-10T09:08:16Z2024-02-29T07:59:40ZLand, jet stream, and other atmospheric effects on burned area estimation during the South Asian heatwave of 2022
Irawan, Amir Mustofa; Vall-Llossera Ferran, Mercedes Magdalena; López Martínez, Carlos; Camps Carmona, Adriano José; Chaparro Danon, David; Portal González, Gerard; Pablos Hernández, Miriam; Alonso González, Alberto
Understanding the key variables that characterise fire propagation is important for a better estimation of fire events and their impacts. This study uses machine learning combined with satellite remote sensing and atmospheric modelled data to enhance estimations of burned areas. It focuses on the intense early summer weather patterns in South Asia during April and May 2022 and explores the relationship between environmental factors and fire spread. The study employs various algorithms, including random forest, extra trees, extreme gradient boosting (XGBoost), gradient boosting regressor, support vector regressor and neural networks. XGBoost proves to be the most accurate approach. An isolation forest algorithm is used to adjust for outliers in burned area estimations. The comprehensive analysis conducted includes the identification of key variables and sensitivity tests incorporating changes of up to 25 % in natural environmental conditions to assess the model’s consistency. The results indicate that integrating vegetation, atmospheric, and human-related variables with the XGBoost algorithm, and incorporating outlier adjustments leads to the most effective performance (R2 ≥ 0.7), with jet stream variables enhancing the accuracy by approximately 11.5 %. The study highlights the notable impact on fire propagation of increases in the value of 300-hPa meridional circulation index flow (MCI300) and a high 500-hPa geopotential height anomaly (ΔZ500), indicating the development of strong atmospheric blocking (upper tropospheric ridge). As compared to other factors, e.g. land surface temperature, vapour pressure deficit, soil moisture and vegetation optical depth, the impact of changes in jet stream metrics (MCI300 and ΔZ500) was more pronounced, indicating greater sensitivity. These insights emphasise the complexity of fire spread, and the importance of using atmospheric factors to estimate burned areas, particularly during severe heatwaves.
2024-02-29T07:59:40ZIrawan, Amir MustofaVall-Llossera Ferran, Mercedes MagdalenaLópez Martínez, CarlosCamps Carmona, Adriano JoséChaparro Danon, DavidPortal González, GerardPablos Hernández, MiriamAlonso González, AlbertoUnderstanding the key variables that characterise fire propagation is important for a better estimation of fire events and their impacts. This study uses machine learning combined with satellite remote sensing and atmospheric modelled data to enhance estimations of burned areas. It focuses on the intense early summer weather patterns in South Asia during April and May 2022 and explores the relationship between environmental factors and fire spread. The study employs various algorithms, including random forest, extra trees, extreme gradient boosting (XGBoost), gradient boosting regressor, support vector regressor and neural networks. XGBoost proves to be the most accurate approach. An isolation forest algorithm is used to adjust for outliers in burned area estimations. The comprehensive analysis conducted includes the identification of key variables and sensitivity tests incorporating changes of up to 25 % in natural environmental conditions to assess the model’s consistency. The results indicate that integrating vegetation, atmospheric, and human-related variables with the XGBoost algorithm, and incorporating outlier adjustments leads to the most effective performance (R2 ≥ 0.7), with jet stream variables enhancing the accuracy by approximately 11.5 %. The study highlights the notable impact on fire propagation of increases in the value of 300-hPa meridional circulation index flow (MCI300) and a high 500-hPa geopotential height anomaly (ΔZ500), indicating the development of strong atmospheric blocking (upper tropospheric ridge). As compared to other factors, e.g. land surface temperature, vapour pressure deficit, soil moisture and vegetation optical depth, the impact of changes in jet stream metrics (MCI300 and ΔZ500) was more pronounced, indicating greater sensitivity. These insights emphasise the complexity of fire spread, and the importance of using atmospheric factors to estimate burned areas, particularly during severe heatwaves.Minimum error entropy estimation under contaminated Gaussian noiseLópez Molina, Carlos AlejandroCabrera Estanyol, Ferran deRiba Sagarra, Jaumehttp://hdl.handle.net/2117/4026202024-02-26T00:22:38Z2024-02-22T07:55:20ZMinimum error entropy estimation under contaminated Gaussian noise
López Molina, Carlos Alejandro; Cabrera Estanyol, Ferran de; Riba Sagarra, Jaume
It is shown that Rényi's entropy of a Gaussian mixture with entropic index α∈(1,∞] is upper-bounded by the cluster with minimum variance. This basic idea leads to a clean worst-case formulation of the minimum error entropy principle in the context of linear multi-sensor fusion by using a largely contaminated Gaussian distribution to model sensor errors with outliers. The obtained entropic best linear unbiased estimator leads to an operational interpretation in terms of a precision/reliability trade-off, it resonates closely with model-order selection methods, and it provides a possible information-theoretic root to sparsity-promoting regularization.
2024-02-22T07:55:20ZLópez Molina, Carlos AlejandroCabrera Estanyol, Ferran deRiba Sagarra, JaumeIt is shown that Rényi's entropy of a Gaussian mixture with entropic index α∈(1,∞] is upper-bounded by the cluster with minimum variance. This basic idea leads to a clean worst-case formulation of the minimum error entropy principle in the context of linear multi-sensor fusion by using a largely contaminated Gaussian distribution to model sensor errors with outliers. The obtained entropic best linear unbiased estimator leads to an operational interpretation in terms of a precision/reliability trade-off, it resonates closely with model-order selection methods, and it provides a possible information-theoretic root to sparsity-promoting regularization.Magnetoelectric dipole antenna framework supporting orbital angular momentum modesJofre Cruanyes, MarcAkazzim, YounessBlanch Boris, SebastiánRomeu Robert, JordiCetiner, Bedri ArtugJofre Roca, Lluíshttp://hdl.handle.net/2117/4022732024-02-20T07:40:13Z2024-02-20T07:31:46ZMagnetoelectric dipole antenna framework supporting orbital angular momentum modes
Jofre Cruanyes, Marc; Akazzim, Youness; Blanch Boris, Sebastián; Romeu Robert, Jordi; Cetiner, Bedri Artug; Jofre Roca, Lluís
The ability to utilize resources to meet the need of growing diversity of communication services and user behavior marks the future of cognitive wireless communication systems. Cognitive wireless technologies for vehicular communications, in combination with Orbital Angular Momentum (OAM) modes aim at extending Non-Line-Of-Sight (NLOS) short-distance communications for smart mobility. In this regard, OAM antenna frameworks need to be developed to support these technologies. In this work, we describe a magnetoelectric dipole antenna framework supporting OAM modes. The framework is derived from moment tensors of specific vector spherical harmonic functions synthesized from dipoles. The antenna framework is discussed in terms of OAM generation, and it is validated numerically and experimentally for l = 1 OAM mode, achieving more than 500MHz operation bandwidth at the frequency of operation of 3.5GHz. Also, for l = 1 OAM mode, the null aligns precisely with the anticipated dimensions numerically computed.
2024-02-20T07:31:46ZJofre Cruanyes, MarcAkazzim, YounessBlanch Boris, SebastiánRomeu Robert, JordiCetiner, Bedri ArtugJofre Roca, LluísThe ability to utilize resources to meet the need of growing diversity of communication services and user behavior marks the future of cognitive wireless communication systems. Cognitive wireless technologies for vehicular communications, in combination with Orbital Angular Momentum (OAM) modes aim at extending Non-Line-Of-Sight (NLOS) short-distance communications for smart mobility. In this regard, OAM antenna frameworks need to be developed to support these technologies. In this work, we describe a magnetoelectric dipole antenna framework supporting OAM modes. The framework is derived from moment tensors of specific vector spherical harmonic functions synthesized from dipoles. The antenna framework is discussed in terms of OAM generation, and it is validated numerically and experimentally for l = 1 OAM mode, achieving more than 500MHz operation bandwidth at the frequency of operation of 3.5GHz. Also, for l = 1 OAM mode, the null aligns precisely with the anticipated dimensions numerically computed.Characterisation, symptom pattern and symptom clusters from a retrospective cohort of Long COVID patients in primary care in CataloniaTorrell Vallespin, GemmaPuente Baliarda, DianaJacques Aviñó, ConstanzaCarrasco Ribelles, Lucía AmaliaViolán Fors, ConcepciónLópez Jiménez, TomásRoyano García, VerónicaMolina Cantón, AlbaMedina Perucha, LauraRodríguez Giralt, IsraelBerenguera Ossó, Annahttp://hdl.handle.net/2117/4008382024-02-04T23:08:27Z2024-02-02T09:59:41ZCharacterisation, symptom pattern and symptom clusters from a retrospective cohort of Long COVID patients in primary care in Catalonia
Torrell Vallespin, Gemma; Puente Baliarda, Diana; Jacques Aviñó, Constanza; Carrasco Ribelles, Lucía Amalia; Violán Fors, Concepción; López Jiménez, Tomás; Royano García, Verónica; Molina Cantón, Alba; Medina Perucha, Laura; Rodríguez Giralt, Israel; Berenguera Ossó, Anna
Background:
Around 10% of people infected by SARS-COV-2 report symptoms that persist longer than 3 months. Little has been reported about sex differences in symptoms and clustering over time of non-hospitalised patients in primary care settings.
Methods:
This is a descriptive study of a cohort of mainly non-hospitalized patients with a persistence of symp- toms longer than 3 months from the clinical onset in co-creation with the Long Covid Catalan affected group using an online survey. Recruitment was from March 2020 to June 2021. Exclusion criteria were being admitted to an ICU, < 18 years of age and not living in Catalonia. We focused on 117 symptoms gathered in 18 groups and performed cluster analysis over the first 21 days of infection, at 22–60 days, and = 3 months.
Results:
We analysed responses of 905 participants (80.3% women). Median time between symptom onset and the questionnaire response date was 8.7 months. General symptoms (as fatigue) were the most prevalent with no differences by sex, age, or wave although its frequency decreased over time (from 91.8 to 78.3%). Dermato- logical (52.1% in women, 28.5% in men), olfactory (34.9% women, 20.9% men) and neurocognitive symptoms (70.1% women, 55.8% men) showed the greatest differences by sex. Cluster analysis showed five clusters with a predomi- nance of Taste & smell (24.9%) and Multisystemic clusters (26.5%) at baseline and _Multisystemic (34.59%) and Heteroge- neous (24.0%) at =3 months. The Multisystemic cluster was more prevalent in men. The Menstrual cluster was the most stable over time, while most transitions occurred from the Heterogeneous cluster to the Multisystemic cluster and from Taste & smell to Heterogeneous.
Conclusions:
General symptoms were the most prevalent in both sexes at three-time cut-off points. Major sex differ- ences were observed in dermatological, olfactory and neurocognitive symptoms. The increase of the Heterogeneous cluster might suggest an adaptation to symptoms or a non-specific evolution of the condition which can hinder its detection at medical appointments. A carefully symptom collection and patients’ participation in research may gener- ate useful knowledge about Long Covid presentation in primary care settings
2024-02-02T09:59:41ZTorrell Vallespin, GemmaPuente Baliarda, DianaJacques Aviñó, ConstanzaCarrasco Ribelles, Lucía AmaliaViolán Fors, ConcepciónLópez Jiménez, TomásRoyano García, VerónicaMolina Cantón, AlbaMedina Perucha, LauraRodríguez Giralt, IsraelBerenguera Ossó, AnnaBackground:
Around 10% of people infected by SARS-COV-2 report symptoms that persist longer than 3 months. Little has been reported about sex differences in symptoms and clustering over time of non-hospitalised patients in primary care settings.
Methods:
This is a descriptive study of a cohort of mainly non-hospitalized patients with a persistence of symp- toms longer than 3 months from the clinical onset in co-creation with the Long Covid Catalan affected group using an online survey. Recruitment was from March 2020 to June 2021. Exclusion criteria were being admitted to an ICU, < 18 years of age and not living in Catalonia. We focused on 117 symptoms gathered in 18 groups and performed cluster analysis over the first 21 days of infection, at 22–60 days, and = 3 months.
Results:
We analysed responses of 905 participants (80.3% women). Median time between symptom onset and the questionnaire response date was 8.7 months. General symptoms (as fatigue) were the most prevalent with no differences by sex, age, or wave although its frequency decreased over time (from 91.8 to 78.3%). Dermato- logical (52.1% in women, 28.5% in men), olfactory (34.9% women, 20.9% men) and neurocognitive symptoms (70.1% women, 55.8% men) showed the greatest differences by sex. Cluster analysis showed five clusters with a predomi- nance of Taste & smell (24.9%) and Multisystemic clusters (26.5%) at baseline and _Multisystemic (34.59%) and Heteroge- neous (24.0%) at =3 months. The Multisystemic cluster was more prevalent in men. The Menstrual cluster was the most stable over time, while most transitions occurred from the Heterogeneous cluster to the Multisystemic cluster and from Taste & smell to Heterogeneous.
Conclusions:
General symptoms were the most prevalent in both sexes at three-time cut-off points. Major sex differ- ences were observed in dermatological, olfactory and neurocognitive symptoms. The increase of the Heterogeneous cluster might suggest an adaptation to symptoms or a non-specific evolution of the condition which can hinder its detection at medical appointments. A carefully symptom collection and patients’ participation in research may gener- ate useful knowledge about Long Covid presentation in primary care settingsDemonstration of continuous multiple access with homodyne and image-rejection heterodyne coherent receivers using directly modulated laser transmittersMasanas Jiménez, MiquelTabares Giraldo, JeisonCano Valadez, Iván NicolásPrat Gomà, Josep Joanhttp://hdl.handle.net/2117/4002192024-01-28T22:22:13Z2024-01-25T07:35:20ZDemonstration of continuous multiple access with homodyne and image-rejection heterodyne coherent receivers using directly modulated laser transmitters
Masanas Jiménez, Miquel; Tabares Giraldo, Jeison; Cano Valadez, Iván Nicolás; Prat Gomà, Josep Joan
This work presents a comprehensive set of experiments for the multipoint-to-point coherent passive optical network (PON), where the wavelength locking between the optical network unit (ONU) and optical line termination lasers is critical, especially if operating in burst mode. Here, we test the performance of continuous multiple access in a splitter-based PON with both an ultra-dense wavelength division multiplexing and RF-subcarrier multiplexing configuration, with simple distributed feedback (DFB) lasers along non-return to zero and pulse amplitude modulation of four levels. Most interestingly, we test a spectrally efficient heterodyne receiver with image-frequency rejection and polarization independence based on the 3×3 optical front-end. Two users at the same intermediate frequency are detected simultaneously avoiding image frequency interference while minimizing complexity, with transmissions of 2.5 Gb/s. We provide comparison with an asynchronous homodyne receiver. The achieved results demonstrate the feasibility of continuous multiple access using thermally controlled ONUs with conventional DFBs as an enhanced alternative to commercial time division multiplexing access.
2024-01-25T07:35:20ZMasanas Jiménez, MiquelTabares Giraldo, JeisonCano Valadez, Iván NicolásPrat Gomà, Josep JoanThis work presents a comprehensive set of experiments for the multipoint-to-point coherent passive optical network (PON), where the wavelength locking between the optical network unit (ONU) and optical line termination lasers is critical, especially if operating in burst mode. Here, we test the performance of continuous multiple access in a splitter-based PON with both an ultra-dense wavelength division multiplexing and RF-subcarrier multiplexing configuration, with simple distributed feedback (DFB) lasers along non-return to zero and pulse amplitude modulation of four levels. Most interestingly, we test a spectrally efficient heterodyne receiver with image-frequency rejection and polarization independence based on the 3×3 optical front-end. Two users at the same intermediate frequency are detected simultaneously avoiding image frequency interference while minimizing complexity, with transmissions of 2.5 Gb/s. We provide comparison with an asynchronous homodyne receiver. The achieved results demonstrate the feasibility of continuous multiple access using thermally controlled ONUs with conventional DFBs as an enhanced alternative to commercial time division multiplexing access.The 2021 la Palma volcanic eruption and its impact on ionospheric scintillation as measured from GNSS reference stations, GNSS-R and GNSS-ROMolina Ordóñez, CarlosBoudriki Semlali, Badr EddineGonzález Casado, GuillermoHyuk, ParkCamps Carmona, Adriano Joséhttp://hdl.handle.net/2117/3997872024-01-21T19:34:28Z2024-01-18T11:44:11ZThe 2021 la Palma volcanic eruption and its impact on ionospheric scintillation as measured from GNSS reference stations, GNSS-R and GNSS-RO
Molina Ordóñez, Carlos; Boudriki Semlali, Badr Eddine; González Casado, Guillermo; Hyuk, Park; Camps Carmona, Adriano José
Ionospheric disturbances induced by seismic activity have been studied in recent years by many authors, showing an impact both before and after the occurrence of earthquakes. In this study, the ionospheric scintillation produced by the 2021 La Palma volcano eruption is analyzed. The Cumbre Vieja volcano was active from 19 September to 13 December 2021, and many earthquakes of magnitude 3–4 were recorded, with some of them reaching magnitude 5. Three methods, GNSS reference monitoring, GNSS reflectometry (GNSS-R) from NASA CYGNSS, and GNSS radio occultation (GNSS-RO) from COSMIC and Spire constellations, are used to compare and evaluate their sensitivity as proxies of earthquakes associated with the volcanic eruption. To compare the seismic activity with ionospheric scintillation, seismic energy release, and 95th percentile of the intensity scintillation parameter (S4), measurements have been computed at 6 h intervals for the whole duration of the volcanic eruption. GNSS-RO has shown the best correlation between earthquake energy and S4, with values up to 0.09 when the perturbations occur around 18 h after the seismic activity. GNSS reference monitoring station data also show some correlation 18 h and 7–8 d after. As expected, GNSS-R is the one that shows the smallest correlation, as the ionospheric signatures get masked by the signature of the surface where the reflection is taking place. Additionally, the three methods show a smaller correlation during the week before earthquakes. Given the small magnitude of the seismic activity, the correlation is barely detectable in this situation, and thus would be difficult to use in any application to find earthquake proxies.
2024-01-18T11:44:11ZMolina Ordóñez, CarlosBoudriki Semlali, Badr EddineGonzález Casado, GuillermoHyuk, ParkCamps Carmona, Adriano JoséIonospheric disturbances induced by seismic activity have been studied in recent years by many authors, showing an impact both before and after the occurrence of earthquakes. In this study, the ionospheric scintillation produced by the 2021 La Palma volcano eruption is analyzed. The Cumbre Vieja volcano was active from 19 September to 13 December 2021, and many earthquakes of magnitude 3–4 were recorded, with some of them reaching magnitude 5. Three methods, GNSS reference monitoring, GNSS reflectometry (GNSS-R) from NASA CYGNSS, and GNSS radio occultation (GNSS-RO) from COSMIC and Spire constellations, are used to compare and evaluate their sensitivity as proxies of earthquakes associated with the volcanic eruption. To compare the seismic activity with ionospheric scintillation, seismic energy release, and 95th percentile of the intensity scintillation parameter (S4), measurements have been computed at 6 h intervals for the whole duration of the volcanic eruption. GNSS-RO has shown the best correlation between earthquake energy and S4, with values up to 0.09 when the perturbations occur around 18 h after the seismic activity. GNSS reference monitoring station data also show some correlation 18 h and 7–8 d after. As expected, GNSS-R is the one that shows the smallest correlation, as the ionospheric signatures get masked by the signature of the surface where the reflection is taking place. Additionally, the three methods show a smaller correlation during the week before earthquakes. Given the small magnitude of the seismic activity, the correlation is barely detectable in this situation, and thus would be difficult to use in any application to find earthquake proxies.Assessment of the impact of long integration time in Geosynchronous SAR imagery of agricultural fields by means of GB-SAR dataAguasca Solé, AlbertoBroquetas Ibars, AntoniLópez Sánchez, Juan ManuelFabregas Canovas, Francisco JavierMallorquí Franquet, Jordi JoanMas i Méndez, Mireiahttp://hdl.handle.net/2117/3984512023-12-21T10:19:22Z2023-12-21T10:17:49ZAssessment of the impact of long integration time in Geosynchronous SAR imagery of agricultural fields by means of GB-SAR data
Aguasca Solé, Alberto; Broquetas Ibars, Antoni; López Sánchez, Juan Manuel; Fabregas Canovas, Francisco Javier; Mallorquí Franquet, Jordi Joan; Mas i Méndez, Mireia
Geosynchronous synthetic aperture radar (GeoSAR) missions offer the advantage of near-continuous monitoring of specific regions on Earth, making them essential for applications that require continuous information. However, wind-induced motion along the inherent long integration time can result in image defocusing, with potential degradation of retrieved information. This article aims to investigate the impact of GeoSAR long integration time in synthetic aperture radar (SAR) imaging and derived products (time series of backscatter and coherence) required to extract agriculture-relevant soil or crop parameters of interest. The study is based on the extensive HydroSoil data acquisition campaign carried out over barley and corn crops, funded by the European Space Agency. The collected raw data are used to synthesize equivalent apertures with integration times of up to 4 h, similar to those acquired with a GeoSAR. These ultraslow apertures facilitate the assessment of the impact of agricultural scene decorrelation on the generation of images with extended integration times.
2023-12-21T10:17:49ZAguasca Solé, AlbertoBroquetas Ibars, AntoniLópez Sánchez, Juan ManuelFabregas Canovas, Francisco JavierMallorquí Franquet, Jordi JoanMas i Méndez, MireiaGeosynchronous synthetic aperture radar (GeoSAR) missions offer the advantage of near-continuous monitoring of specific regions on Earth, making them essential for applications that require continuous information. However, wind-induced motion along the inherent long integration time can result in image defocusing, with potential degradation of retrieved information. This article aims to investigate the impact of GeoSAR long integration time in synthetic aperture radar (SAR) imaging and derived products (time series of backscatter and coherence) required to extract agriculture-relevant soil or crop parameters of interest. The study is based on the extensive HydroSoil data acquisition campaign carried out over barley and corn crops, funded by the European Space Agency. The collected raw data are used to synthesize equivalent apertures with integration times of up to 4 h, similar to those acquired with a GeoSAR. These ultraslow apertures facilitate the assessment of the impact of agricultural scene decorrelation on the generation of images with extended integration times.Quasi-3D model for lateral resonances on homogeneous BAW resonatorsUdaondo Guerrero, CarlosCollado Gómez, Juan CarlosMateu Mateu, Jordihttp://hdl.handle.net/2117/3984502023-12-24T22:27:14Z2023-12-21T09:55:09ZQuasi-3D model for lateral resonances on homogeneous BAW resonators
Udaondo Guerrero, Carlos; Collado Gómez, Juan Carlos; Mateu Mateu, Jordi
Lateral modes are responsible for the in-band spurious resonances that appear on BAW resonators, degrading the in-band filter response. In this work, a fast computational method based on the transmission line matrix (TLM) method is employed to model the lateral resonances of BAW resonators. Using the precomputed dispersion curves of Lamb waves and an equivalent characteristic impedance for the TE1 mode, a network of transmission lines is used to calculate the magnitude of field distributions on the electrodes. These characteristics are specific to the stack layer configuration. The model’s implementation is based on nodal Y matrices, from which particle displacement profiles are coupled to the electric domain via piezoelectric constitutive relations. Consequently, the input impedance of the resonator is obtained. The model exhibits strong agreement with FEM simulations of FBARs and SMRs, and with measurements of several SMRs. The proposed model can provide accurate predictions of resonator input impedance, which is around 200 times faster than conventional FEM.
2023-12-21T09:55:09ZUdaondo Guerrero, CarlosCollado Gómez, Juan CarlosMateu Mateu, JordiLateral modes are responsible for the in-band spurious resonances that appear on BAW resonators, degrading the in-band filter response. In this work, a fast computational method based on the transmission line matrix (TLM) method is employed to model the lateral resonances of BAW resonators. Using the precomputed dispersion curves of Lamb waves and an equivalent characteristic impedance for the TE1 mode, a network of transmission lines is used to calculate the magnitude of field distributions on the electrodes. These characteristics are specific to the stack layer configuration. The model’s implementation is based on nodal Y matrices, from which particle displacement profiles are coupled to the electric domain via piezoelectric constitutive relations. Consequently, the input impedance of the resonator is obtained. The model exhibits strong agreement with FEM simulations of FBARs and SMRs, and with measurements of several SMRs. The proposed model can provide accurate predictions of resonator input impedance, which is around 200 times faster than conventional FEM.RFI mitigation in microwave radiometry using the Karhunen–Loève transformDíez García, RaúlCamps Carmona, Adriano JoséHyuk, Parkhttp://hdl.handle.net/2117/3973502023-12-03T21:19:33Z2023-11-30T07:26:26ZRFI mitigation in microwave radiometry using the Karhunen–Loève transform
Díez García, Raúl; Camps Carmona, Adriano José; Hyuk, Park
This study presents a new signal-processing method to mitigate radio-frequency interference (RFI), based on the Karhunen–Loève transform (KLT). The main property of this method is that it is adaptive and RFI-agnostic, that is, it can potentially mitigate any type of RFI waveform. It is based on the estimation of the covariance matrix of the process, and the derivation of a basis that spans the RFI subspace. The methodology to find this basis is explained in detail. The theoretical performance obtained using KLT is explored by simulations, along with the evaluation of the influence of the system parameters. In addition, a couple of examples with real data are provided.
2023-11-30T07:26:26ZDíez García, RaúlCamps Carmona, Adriano JoséHyuk, ParkThis study presents a new signal-processing method to mitigate radio-frequency interference (RFI), based on the Karhunen–Loève transform (KLT). The main property of this method is that it is adaptive and RFI-agnostic, that is, it can potentially mitigate any type of RFI waveform. It is based on the estimation of the covariance matrix of the process, and the derivation of a basis that spans the RFI subspace. The methodology to find this basis is explained in detail. The theoretical performance obtained using KLT is explored by simulations, along with the evaluation of the influence of the system parameters. In addition, a couple of examples with real data are provided.