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1.	Klionsky, Daniel J, et al. (författare) Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition). 2016 Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 12:1, s. 1-222 Tidskriftsartikel (refereegranskat)
2.	Palm, Bruna, et al. (författare) Autoregressive model for multi-pass SAR change detection based on image stacks 2018 Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 9781510621619 Konferensbidrag (refereegranskat)abstract Change detection is an important synthetic aperture radar (SAR) application, usually used to detect changes on the ground scene measurements in different moments in time. Traditionally, change detection algorithm (CDA) is mainly designed for two synthetic aperture radar (SAR) images retrieved at different instants. However, more images can be used to improve the algorithms performance, witch emerges as a research topic on SAR change detection. Image stack information can be treated as a data series over time and can be modeled by autoregressive (AR) models. Thus, we present some initial findings on SAR change detection based on image stack considering AR models. Applying AR model for each pixel position in the image stack, we obtained an estimated image of the ground scene which can be used as a reference image for CDA. The experimental results reveal that ground scene estimates by the AR models is accurate and can be used for change detection applications. © 2018 SPIE.
3.	Palm, Bruna G, et al. (författare) Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack 2020 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:7 Tidskriftsartikel (refereegranskat)abstract This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0 . 11 / km 2 , when considering military vehicles concealed in a forest.
4.	Palm, Bruna, et al. (författare) Rayleigh Regression Model for Ground Type Detection in SAR Imagery 2019 Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE. - 1545-598X .- 1558-0571. ; 16:10, s. 1660-1664 Tidskriftsartikel (refereegranskat)abstract This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.
5.	Palm, Bruna, et al. (författare) Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers 2022 Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 60 Tidskriftsartikel (refereegranskat)abstract The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This article aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the nonrobust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the nonrobust estimators. Moreover, two SAR datasets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature. © 2022 IEEE.
6.	Alaves, Dimas, et al. (författare) A dynamic hybrid antenna/relay selection scheme for the multiple-access relay channel 2014 Ingår i: 2014 11TH INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATIONS SYSTEMS (ISWCS). - : IEEE. - 9781479958634 ; , s. 594-599 Konferensbidrag (refereegranskat)abstract We propose a dynamic hybrid antenna/relay selection scheme for multiple-access relay systems. The proposed scheme aims to boost the system throughput while keeping a good error performance. By using the channel state information, the destination node performs a dynamic selection between the signals provided by the multi-antenna relay, located in the inter-cell region, and the relay nodes geographically distributed over the cells. The multi-antenna relay and the single-antenna relay nodes employ the decode-remodulate-and-forward and amplify-and-forward protocols, respectively. Results reveal that the proposed scheme offers a good tradeoff between spectral efficiency and diversity gain, which is one of the main requirements for the next generation of wireless communications systems.
7.	Alves, Dimas I, et al. (författare) A Statistical Analysis for Wavelength-Resolution SAR Image Stacks 2020 Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1545-598X .- 1558-0571. ; 17:2, s. 227-231 Tidskriftsartikel (refereegranskat)abstract This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59 & x0025; of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.
8.	Alves, Dimas irion, et al. (författare) Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem : An Iterative Approach 2023 Ingår i: IEEE Access. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 84734-84743 Tidskriftsartikel (refereegranskat)abstract This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author
9.	Alves, Dimas I., et al. (författare) Cooperative multiple-access scheme with antenna selection and incremental relaying 2014 Ingår i: 2014 INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS). - São Paulo : IEEE. - 9781479937431 Konferensbidrag (refereegranskat)abstract A cooperative multiple-access scheme for wireless communications systems with antenna selection and incremental relaying is proposed. The scheme aims to improve the system throughput while preserving good performance in terms of bit error rate. The system consists of N nodes which send their information to both the destination node and the multiple-antenna relay station. Based on the channel state information, the destination node decides whether or not relaying will be performed. When the relaying is performed, the decode-remodulate-and-forward protocol is used with the best antenna. Results reveal that the proposed scheme achieves a good tradeoff between throughput and bit error rate, which makes suitable to be considered for multi-user networks.
10.	Alves, Dimas, I, et al. (författare) Incoherent Change Detection Methods for Wavelength-Resolution SAR Image Stacks Based on Masking Techniques 2020 Ingår i: 2020 IEEE National Radar Conference - Proceedings. - : IEEE. - 9781728189420 Konferensbidrag (refereegranskat)abstract This paper presents two incoherent change detection methods for wavelength-resolution synthetic aperture radars (SAR) image stacks based on masking techniques. The first technique proposed is the Simple Masking Detection (SMD). This method uses the statistical behavior of pixels-sets in the image stack to create a binary mask, which is used to remove pixels that are not related to changes in a surveillance image from the same interest region. The second technique is the Multiple Concatenated Masking Detection (MCMD), which produces a more selective mask than the SMD by concatenating multiple masks from different image stacks. The MCMD can be used in specific applications where multiple stacks share common patterns of target deployments. Both proposed techniques were evaluated using 24 incoherent SAR images obtained by the CARABAS II system. The experimental results revealed that the proposed detection methods have better performance in terms of probability of detection and false alarm rate when compared with other change detection techniques, especially for high detection probabilities scenarios.
11.	Alves, Dimas I., et al. (författare) Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks 2022 Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-598X .- 1558-0571. ; 19 Tidskriftsartikel (refereegranskat)abstract This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman-Pearson criterion. The first proposed method uses the data from wavelength-resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers a priori information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods. CCBY
12.	Alves, Dimas I, et al. (författare) Statistical Analysis for Wavelength-Resolution SARImage Stacks : New Case Studies 2020 Ingår i: XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS. Konferensbidrag (refereegranskat)abstract This paper presents new case studies for thestatistical analysis for wavelength resolution SAR image stacks.The statistical analysis considers the Anderson-Darling goodnessof-fit test in a set of pixel samples from the same position obtainedfrom a SAR image stack. The test is applied in wavelengthresolution SAR image stacks. The present work consists of twocase studies based on the use of multiple-pass stacks and TypeI error using the False Discovery Rate controlling procedures.In addition, an application scenario is presented for the studiedscenarios.
13.	Alves, Dimas Irion, et al. (författare) Wavelength-Resolution SAR Change Detection Using Bayes' Theorem 2020 Ingår i: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1939-1404 .- 2151-1535. ; 13, s. 5560-5568 Tidskriftsartikel (refereegranskat)abstract This article presents Bayes' theorem for wavelength-resolution synthetic aperture radar (SAR) change detection method development. Different change detection methods can be derived using Bayes' theorem in combination with the target model, clutter-plus-noise model, iterative implementation, and noniterative implementation. As an example of the Bayes' theorem use for wavelength-resolution SAR change detection method development, we propose a simple change detection method with a clutter-plus-noise model and noniterative implementation. In spite of simplicity, the proposed method provides a very competitive performance in terms of probability of detection and false alarm rate. The best result was a probability of detection of $\text{98.7}\%$ versus a false alarm rate of one per square kilometer.
14.	Alves Machado Filho, Manoel, et al. (författare) Self-Induced Core–Shell InAlN Nanorods: Formation and Stability Unraveled by Ab Initio Simulations 2023 Ingår i: ACS Nanoscience Au. - : American Chemical Society (ACS). - 2694-2496. ; 3:1, s. 84-93 Tidskriftsartikel (refereegranskat)abstract By addressing precursor prevalence and energetics using the DFT-based synthetic growth concept (SGC), the formation mechanism of self-induced InAlN core–shell nanorods (NRs) synthesized by reactive magnetron sputter epitaxy (MSE) is explored. The characteristics of In- and Al-containing precursor species are evaluated considering the thermal conditions at a typical NR growth temperature of around 700 °C. The cohesive and dissociation energies of In-containing precursors are consistently lower than those of their Al-containing counterparts, indicating that In-containing precursors are more weakly bonded and more prone to dissociation. Therefore, In-containing species are expected to exhibit lower abundance in the NR growth environment. At increased growth temperatures, the depletion of In-based precursors is even more pronounced. A distinctive imbalance in the incorporation of Al- and In-containing precursor species (namely, AlN/AlN+, AlN2/AlN2+, Al2N2/Al2N2+, and Al2/Al2+ vs InN/InN+, InN2/InN2+, In2N2/In2N2+, and In2/In2+) is found at the growing edge of the NR side surfaces, which correlates well with the experimentally obtained core–shell structure as well as with the distinctive In-rich core and vice versa for the Al-rich shell. The performed modeling indicates that the formation of the core–shell structure is substantially driven by the precursors’ abundance and their preferential bonding onto the growing edge of the nanoclusters/islands initiated by phase separation from the beginning of the NR growth. The cohesive energies and the band gaps of the NRs show decreasing trends with an increment in the In concentration of the NRs’ core and with an increment in the overall thickness (diameter) of the NRs. These results reveal the energy and electronic reasons behind the limited growth (up to ∼25% of In atoms of all metal atoms, i.e., InxAl1–xN, x ∼ 0.25) in the NR core and may be qualitatively perceived as a limiting factor for the thickness of the grown NRs (typically <50 nm).
15.	Amora-Nogueira, Leonardo, et al. (författare) Correction: Tropical forests as drivers of lake carbon burial (vol 13, 4051, 2022) 2023 Ingår i: Nature Communications. - : NATURE PORTFOLIO. - 2041-1723. ; 14:1 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
16.	Amora-Nogueira, Leonardo, et al. (författare) Tropical forests as drivers of lake carbon burial 2022 Ingår i: Nature Communications. - : Nature Portfolio. - 2041-1723. ; 13:1 Tidskriftsartikel (refereegranskat)abstract A significant proportion of carbon (C) captured by terrestrial primary production is buried in lacustrine ecosystems, which have been substantially affected by anthropogenic activities globally. However, there is a scarcity of sedimentary organic carbon (OC) accumulation information for lakes surrounded by highly productive rainforests at warm tropical latitudes, or in response to land cover and climate change. Here, we combine new data from intensive campaigns spanning 13 lakes across remote Amazonian regions with a broad literature compilation, to produce the first spatially-weighted global analysis of recent OC burial in lakes (over ~50-100-years) that integrates both biome type and forest cover. We find that humid tropical forest lake sediments are a disproportionately important global OC sink of 7.4 Tg C yr−1 with implications for climate change. Further, we demonstrate that temperature and forest conservation are key factors in maintaining massive organic carbon pools in tropical lacustrine sediments.
17.	Araujo, Gustavo F., et al. (författare) A Tailored cGAN SAR Synthetic Data Augmentation Method for ATR Application 2023 Ingår i: Proceedings of the IEEE Radar Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665436694 Konferensbidrag (refereegranskat)abstract This article proposes a method to simulate Synthetic Aperture Radar (SAR) targets for specific incidence and azimuth angles. Images synthesized by Electromagnetic Computing (EMC) are used to train a Conditional Generative Adversarial Network (cGAN). Two synthetic image chips of the same class and incidence angle, separated by two degrees in azimuth, are used as input to the cGAN. The cGAN predicts the image of the same class and incidence angle whose azimuth angle corresponds to the bisector of the two input chips. An evaluation using the SAMPLE dataset was performed to verify the quality of the image prediction. Running through a total of 100 training epochs, the cGAN converges, reaching the best Mean Squared Error (MSE) after 77 epochs. The results demonstrate that the proposed method is promising for Automatic Target Recognition (ATR) applications. © 2023 IEEE.
18.	Araujo, Gustavo F., et al. (författare) Assessment of preprocessing techniques in a model-based automatic target recognition algorithm for the SAMPLE dataset 2022 Ingår i: Image and Signal Processing for Remote Sensing XXVIII 2022. - : SPIE - International Society for Optical Engineering. - 9781510655379 Konferensbidrag (refereegranskat)abstract This article investigates basic preprocessing techniques to improve classification accuracy in the context of Automatic Target Recognition (ATR) of non-cooperative targets in Synthetic Aperture Radar (SAR) images. Preprocessing techniques are considered in synthetic data providing different inputs to a model-based classification algorithm. Experiments with preprocessing techniques such as area reduction, morphological transformations, and speckle filtering were run using ten target classes of the SAMPLE dataset. The classification is performed in measure data using scattering centers as features. The results reveal that the original image without any preprocessing techniques reached the best classification performance. However, investigations with other classifiers that use different features may benefit from such preprocessing techniques. © 2022 SPIE.
19.	Araujo, Gustavo F., et al. (författare) Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models 2022 Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:3 Tidskriftsartikel (refereegranskat)abstract This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than −5 dB. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
20.	Araujo, Gustavo F., et al. (författare) Synthetic SAR Data Generator Using Pix2pix cGAN Architecture for Automatic Target Recognition 2023 Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 143369-143386 Tidskriftsartikel (refereegranskat)abstract Synthetic Aperture Radar (SAR) technology has unique advantages but faces challenges in obtaining enough data for noncooperative target classes. We propose a method to generate synthetic SAR data using a modified pix2pix Conditional Generative Adversarial Networks (cGAN) architecture. The cGAN is trained to create synthetic SAR images with specific azimuth and elevation angles, demonstrating its capability to closely mimic authentic SAR imagery through convergence and collapsing analyses. The study uses a model-based algorithm to assess the practicality of the generated synthetic data for Automatic Target Recognition (ATR). The results reveal that the classification accuracy achieved with synthetic data is comparable to that attained with original data, highlighting the effectiveness of the proposed method in mitigating the limitations imposed by noncooperative SAR data scarcity for ATR. This innovative approach offers a promising solution to craft customized synthetic SAR data, ultimately enhancing ATR performance in remote sensing.
21.	Barbosa, V.L., et al. (författare) Linear Array Design with Switched Beams for Wireless Communications Systems 2015 Ingår i: International Journal of Antennas and Propagation. - : Hindawi. - 1687-5869 .- 1687-5877. ; 2015 Tidskriftsartikel (refereegranskat)abstract This paper presents an analysis for optimal design of switched beamforming applied to a linear array for wireless communication systems. The beam switching scheme provides coverage of a given sector in azimuth and controls the sidelobe level simultaneously. The analysis was developed considering arrays composed of Quasi-Yagi elements. The model assumes a user moving in the azimuthal direction under a constant velocity and with an estimation of the signal-to-noise ratio (SNR) at the mobile user (MU). The radio base station applies the beam that yields the best performance during transmission. The decision is based on the feedback information received from the MU. The goal of the analysis is to determine the best trade-off between the array size and number of feedback bits necessary to maximize the SNR at the receiver. The results show that a compromise between the number of beam-pointing directions and the array size should be taken into consideration for a wireless communication system design.
22.	Campos, Alexandre B., et al. (författare) Adaptive Target Enhancer : Bridging the Gap between Synthetic and Measured SAR Images for Automatic Target Recognition 2023 Ingår i: Proceedings of the IEEE Radar Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665436694 Konferensbidrag (refereegranskat)abstract Automatic target recognition (ATR) algorithms have been successfully used for vehicle classification in synthetic aperture radar (SAR) images over the past few decades. For this application, however, the scarcity of labeled data is often a limiting factor for supervised approaches. While the advent of computer-simulated images may result in additional data for ATR, there is still a substantial gap between synthetic and measured images. In this paper, we propose the so-called adaptive target enhancer (ATE), a tool designed to automatically delimit and weight the region of an image that contains or is affected by the presence of a target. Results for the publicly released Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset show that, by defining regions of interest and suppressing the background, we can increase the classification accuracy from 68% to 84% while only using artificially generated images for training. © 2023 IEEE.
23.	Campos, Alexandre Becker, 1997-, et al. (författare) False Alarm Reduction in Wavelength-Resolution SAR Change Detection Schemes by Using a Convolutional Neural Network 2022 Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-598X .- 1558-0571. ; 19 Tidskriftsartikel (refereegranskat)abstract In this letter, we propose a method to reduce the number of false alarms in a wavelength-resolution synthetic aperture radar (SAR) change detection scheme by using a convolutional neural network (CNN). The detection is performed in two steps: change analysis and object classification. A simple technique for wavelength-resolution SAR change detection is implemented to extract potential targets from the image of interest. A CNN is then used for classifying the change map detections as either a target or nontarget, further reducing the false alarm rate (FAR). The scheme is tested for the CARABAS-II data set, where only three false alarms over a testing area of 96 km² are reported while still sustaining a probability of detection above 96%. We also show that the network can still reduce the FAR even when the flight heading of the SAR system measurement campaign differs by up to 100° between the images used for training and test. CCBY
24.	Campos, Alexandre Becker, 1997-, et al. (författare) UNSUPERVISED AUTOMATIC TARGET DETECTION FOR MULTITEMPORAL SAR IMAGES BASED ON ADAPTIVE K-MEANS ALGORITHM 2020 Konferensbidrag (refereegranskat)abstract In this paper, we present an unsupervised automatic target detection algorithm for multitemporal SAR images. The proposed two-fold method is expected to reduce processing time for large scene sizes with sparse targets while still improving detection performance. Firstly, pixel blocks are extracted from an initial change map to reduce the algorithm's search space and favor target detection. Secondly, an adaptive k-means algorithm selects the number of clusters that better separates targets from false alarms, which are discarded. Preliminary results show the advantages of the proposed method in processing time and detection performance over a recently proposed supervised method for the CARABAS-II dataset.
25.	da Silva, Fabiano G., et al. (författare) Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images 2022 Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 14:13 Tidskriftsartikel (refereegranskat)abstract This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
26.	da Silva, Fabiano G., et al. (författare) Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery 2022 Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE - International Society for Optical Engineering. Konferensbidrag (refereegranskat)abstract Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers. © 2022 SPIE.
27.	da Silva, Fabiano Gabriel, et al. (författare) Hyperparameters Analysis of Machine Learning Techniques for Classification of Marine Targets in SAR Images 2023 Ingår i: Proceedings of the XX Brazilian Symposium on Remote Sensing. - 9786589159049 ; , s. 1095-1098 Konferensbidrag (refereegranskat)abstract Due to the extensive coastal area of Brazil, pattern recognition techniques based on artificial intelligence can search for targets at sea faster for surveillance, rescue, or illicit combat activities. This article presents a hyperparameter analysis of machine learning techniques to classify targets in SAR images. We considered a data set with vertical horizontal polarization SAR images from Campos Basin, Rio de Janeiro, to classify oil platforms and ships. The classification attributes are extracted through a convolutional neural network VGG-16 pre-trained with the ImageNet data set. Then, four machine learning techniques are evaluated, random forest, decision tree, k-nearest-neighbors, and logistic regression. As a metric for assessing the classifiers, accuracy (Acc) and area under the curve (AUC) are used. The grid search technique is used to identify the best combination of parameters of the classifiers with the highest Acc and AUC. Finally, the best result is the logistic regression classifier.
28.	El-Sayed, Najib M., et al. (författare) The genome sequence of Trypanosoma cruzi, etiologic agent of Chagas disease. 2005 Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 1095-9203 .- 0036-8075. ; 309:5733, s. 409-15 Tidskriftsartikel (refereegranskat)abstract Whole-genome sequencing of the protozoan pathogen Trypanosoma cruzi revealed that the diploid genome contains a predicted 22,570 proteins encoded by genes, of which 12,570 represent allelic pairs. Over 50% of the genome consists of repeated sequences, such as retrotransposons and genes for large families of surface molecules, which include trans-sialidases, mucins, gp63s, and a large novel family (>1300 copies) of mucin-associated surface protein (MASP) genes. Analyses of the T. cruzi, T. brucei, and Leishmania major (Tritryp) genomes imply differences from other eukaryotes in DNA repair and initiation of replication and reflect their unusual mitochondrial DNA. Although the Tritryp lack several classes of signaling molecules, their kinomes contain a large and diverse set of protein kinases and phosphatases; their size and diversity imply previously unknown interactions and regulatory processes, which may be targets for intervention.
29.	Fabrin, Ana, et al. (författare) A CFAR optimization for low frequency UWB SAR change detection algorithms 2017 Ingår i: International Geoscience and Remote Sensing Symposium (IGARSS). - : Institute of Electrical and Electronics Engineers Inc.. - 9781509049516 ; , s. 1071-1074 Konferensbidrag (refereegranskat)abstract This paper presents a study on the constant false alarm rate (CFAR) filter design for change detection algorithms (CDA). More specifically, we are interested in CFAR filters used in CDA for low frequency ultra-wideband (UWB) synthetic aperture radar (SAR) systems. The filter design performance was evaluated in terms of false alarm rate (FAR) and probability of detection (PD). For evaluation purposes, we considered a set of SAR images obtained with the CARABAS-II system. The results are compared with the ones presented in [1], where the same CDA was considered, except for the CFAR filter. The results show that relevant FAR performance improvements can be obtained by just modifying the CFAR filter parameters taking into account the image resolution and target characteristics. © 2017 IEEE.
30.	Hellisten, Hans, et al. (författare) EXPERIMENTAL RESULTS ON CHANGE DETECTION BASED ON BAYES PROBABILITY THEOREM 2015 Ingår i: 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). - : IEEE Communications Society. - 9781479979295 ; , s. 318-321 Konferensbidrag (refereegranskat)abstract In this paper we propose a new change detection (CD) algorithm based on the Bayes theorem and probability assignments. Differently from any kind of likelihood ratio test (LRT) algorithms, the proposed algorithm does not present target alarms, but the probability of certain image position is a target position. In other words, the proposed method leads to quantitative estimates on the probability of a target at any pixel, whereas LRT algorithms can only be used as a figure of merit for any pixel to contain a target.
31.	Jonas, Sander, et al. (författare) Strategy report for offering traceable calibration for testing machines under continuous and dynamic forces 2023 Rapport (övrigt vetenskapligt/konstnärligt)
32.	Ludwig Barbosa, Vinícius, 1990-, et al. (författare) Beamforming of a Linear Array Applying PSO Algorithm with Restrictive Approach 2016 Ingår i: Journal of Communication and Information Systems. - : Sociedad Brasileira de Telecomunicacoes. - 1980-6604. ; 31:1, s. 118-126 Tidskriftsartikel (refereegranskat)abstract This paper presents a four-element linear arraycomposed of E-shaped microstrip antennas designed to switched-beam application in ISM band. Particle Swarm Optimization(PSO) algorithm is applied to optimize four different sets ofamplitude and progressive phase shift to achieve four distinctradiation patterns controlling the major lobe direction andsidelobe level. For this application, two restrictive approachesare presented for the implementation of PSO algorithm in orderto improve the algorithm convergence to feasible solutions.
33.	Luize, Bruno Garcia, et al. (författare) Geography and ecology shape the phylogenetic composition of Amazonian tree communities 2024 Ingår i: JOURNAL OF BIOGEOGRAPHY. - 0305-0270 .- 1365-2699. Tidskriftsartikel (refereegranskat)abstract Aim: Amazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. Location: Amazonia. Taxon: Angiosperms (Magnoliids; Monocots; Eudicots). Methods: Data for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran's eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. Results: In the terra firme and v & aacute;rzea forest types, the phylogenetic composition varies by geographic region, but the igap & oacute; and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R-2 = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R-2 = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main Conclusion: Numerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions.
34.	Machado Filho, Manoel Alves, et al. (författare) Density Functional Theory-Fed Phase Field Model for Semiconductor Nanostructures: The Case of Self-Induced Core-Shell InAlN Nanorods 2024 Ingår i: Crystal Growth & Design. - : AMER CHEMICAL SOC. - 1528-7483 .- 1528-7505. Tidskriftsartikel (refereegranskat)abstract The self-induced formation of core-shell InAlN nanorods (NRs) is addressed at the mesoscopic scale by density functional theory (DFT)-resulting parameters to develop phase field modeling (PFM). Accounting for the structural, bonding, and electronic features of immiscible semiconductor systems at the nanometer scale, we advance DFT-based procedures for computation of the parameters necessary for PFM simulation runs, namely, interfacial energies and diffusion coefficients. The developed DFT procedures conform to experimental self-induced InAlN NRs' concerning phase-separation, core/shell interface, morphology, and composition. Finally, we infer the prospects for the transferability of the coupled DFT-PFM simulation approach to a wider range of nanostructured semiconductor materials.
35.	Machado, Renato, et al. (författare) EMPIRICAL-STATISTICAL ANALYSIS OF AMPLITUDE SAR IMAGES FOR CHANGE DETECTION ALGORITHMS 2015 Ingår i: 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). - 9781479979295 ; , s. 365-368 Konferensbidrag (refereegranskat)abstract This paper presents an analysis of pre-filtered clutter VHF SAR images. The image data are reorganized into sub-vectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal, which can be a valuable premise for change detection processing.
36.	Miller, Dennis J., et al. (författare) Natural gas hydrates in the Rio Grande Cone (Brazil) : A new province in the western South Atlantic 2015 Ingår i: Marine and Petroleum Geology. - : Elsevier. - 0264-8172 .- 1873-4073. ; 67, s. 187-196 Tidskriftsartikel (refereegranskat)abstract The Rio Grande Cone is a large-scale fanlike feature in the continental slope of the Pelotas Basin, Southern Brazil, where ubiquitous world-class bottom simulating reflectors (BSRs) are readily observed in seismic records. With the purpose of searching for natural gas hydrate deposits in the Cone area, four oceanographic cruises were carried out between May 2011 and July 2013, leading to the discovery of two pockmark fields, active faults and gas hydrates in shallow sediments. Multichannel seismic, multibeam echo sounder, side scan sonar and sub-bottom profiler records were used to map the shallow section and select sites for piston core sampling. Gas hydrates were recovered in several piston cores within muddy sediments collected inside pockmarks displaying high backscatter in the multibeam and side scan sonar data. We present two representative piston cores where numerous levels of gas hydrates occur, along with degassing features, authigenic carbonate and soupy sediments. Gas dissociated from gas hydrate samples is dominantly methane (>99.78%) with minor quantities of ethane. The chemical and isotopic compositions of the gas strongly suggest a biogenic origin for the analyzed samples. These new findings are regarded as strong enough evidence to consider the Rio Grande Cone as a new gas hydrate province. (C) 2015 Elsevier Ltd. All rights reserved.
37.	Mittmann Voigt, Gustavo Henrique, et al. (författare) A Statistical Analysis for Intensity Wavelength-Resolution SAR Difference Images 2023 Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 15:9 Tidskriftsartikel (refereegranskat)abstract This paper presents a statistical analysis of intensity wavelength-resolution synthetic aperture radar (SAR) difference images. In this analysis, Anderson Darling goodness-of-fit tests are performed, considering two different statistical distributions as candidates for modeling the clutter-plus-noise, i.e., the background statistics. The results show that the Gamma distribution is a good fit for the background of the tested SAR images, especially when compared with the Exponential distribution. Based on the results of this statistical analysis, a change detection application for the detection of concealed targets is presented. The adequate selection of the background distribution allows for the evaluated change detection method to achieve a better performance in terms of probability of detection and false alarm rate, even when compared with competitive performance change detection methods in the literature. For instance, in an experimental evaluation considering a data set obtained by the Coherent All Radio Band Sensing (CARABAS) II UWB SAR system, the evaluated change detection method reached a detection probability of 0.981 for a false alarm rate of 1/km2. © 2023 by the authors.
38.	Molin, R.D., et al. (författare) Iterative Change Detection Algorithm for Low-Frequency UWB SAR 2016 Konferensbidrag (refereegranskat)
39.	Molin, Ricardo D., Jr., et al. (författare) A CHANGE DETECTION ALGORITHM FOR SAR IMAGES BASED ON LOGISTIC REGRESSION 2019 Ingår i: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019). - : IEEE. - 9781538691540 ; , s. 1514-1517 Konferensbidrag (refereegranskat)abstract This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, flight heading and neighboring features such as local mean, standard deviation and skewness. The proposed method intends to explore the advantadges from both pixel- and object-based approaches, while evaluating multiple features in amplitude only SAR images. Preliminary results based on K-fold cross validation have shown that the proposed CDA achieves good performance when compared to the results presented in [1].
40.	Moreira, André R., et al. (författare) Classification of Oil Rigs in SAR Images Using RPCA-Based Preprocessing 2024 Ingår i: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. - : Institute of Electrical and Electronics Engineers (IEEE). - 9783800762873 ; , s. 432-437 Konferensbidrag (refereegranskat)abstract This paper uses a signal separation method called Robust Principal Component Analysis (RPCA) as a pre-processing technique to improve the classification of oil rigs in Synthetic Aperture Radar (SAR) images. After the pre-processing method, features are extracted from the images using the VGG-16 convolutional neural network. These features guide classification through Support Vector Machine (SVM), Neural Networks, and Logistic Regression algorithms. The experiments used SAR images from the Sentinel-1 system, C-band, and VH polarization. Early results highlight that preprocessing improves classification accuracy compared to conventional methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.
41.	Pie, Marcio R., et al. (författare) Phylogenetic diversity and the structure of host-epiphyte interactions across the Neotropics 2023 Ingår i: PeerJ. - 2167-8359. ; 11 Tidskriftsartikel (refereegranskat)abstract Understanding the mechanisms driving community assembly has been a major focus of ecological research for nearly a century, yet little is known about these mechanisms in commensal communities, particularly with respect to their historical/evolutionary components. Here, we use a large-scale dataset of 4,440 vascular plant species to explore the relationship between the evolutionary distinctiveness (ED) (as measured by the’species evolutionary history’ (SEH)) of host species and the phylogenetic diversity (PD) of their associated epiphyte species. Although there was considerable variation across hosts and their associated epiphyte species, they were largely unrelated to host SEH. Our results mostly support the idea that the determinants of epiphyte colonization success might involve host characteristics that are unrelated to host SEH (e.g., architectural differences between hosts). While determinants of PD of epiphyte assemblages are poorly known, they do not appear to be related to the evolutionary history of host species. Instead, they might be better explained by neutral processes of colonization and extinction. However, the high level of phylogenetic signal in epiphyte PD (independent of SEH) suggests it might still be influenced by yet unrecognized evolutionary determinants. This study highlights how little is still known about the phylogenetic determinants of epiphyte communities.
42.	Ramos, Lucas P., et al. (författare) A wavelength-resolution sar change detection method based on image stack through robust principal component analysis 2021 Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:5, s. 1-16 Tidskriftsartikel (refereegranskat)abstract Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
43.	Ramos, Lucas P., et al. (författare) Change detection in UWB VHF SAR images exploiting flight heading diversity through robust principal component analysis 2020 Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 9781510638792 Konferensbidrag (refereegranskat)abstract Change detection methods are frequently associated with wavelength-resolution synthetic aperture radar (SAR) images for foliage-penetrating (FOPEN) applications (e.g., the detection of concealed targets in forestry areas), being a research topic of interest over the last decades. The challenge associated with the design of automated change detection techniques goes beyond performing the target detection. It is also related to clutter suppression aiming at a low false alarm rate (FAR). The problem of detecting targets and removing content in SAR data can be treated as an unsupervised signal separation problem, usually referred to as blind source separation (BSS). Additionally, low frequency wavelength-resolution SAR images can be considered to follow an additive separation model due to their backscatter characteristics. In this context, it is possible to explore robust principal component analysis (RPCA) as a source-separation method for problems in which the mixing model is additive and two-dimensional, as the interest SAR images. This paper presents a change detection method for wavelengthresolution SAR images based on the RPCA via principal component pursuit (PCP), considering the use of small image stacks to explore the data diversity from measurements of different flight headings. The proposed method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high frequency (VHF) SAR system CARABAS II. The experimental results show that the proposed method can achieve a high probability of detection (PD) values for a low FAR (i.e., PD of 0.98 for a FAR of 0.41 objects per square kilometer). Finally, discussions regarding the use of the RPCA in change detection methods and the diversity gains are provided in the paper. © SPIE. Downloading of the abstract is permitted for personal use only.
44.	Ramos, Lucas P., et al. (författare) Change Detection in Wavelength-Resolution SAR Image Stack Based on Tensor Robust PCA 2024 Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-598X .- 1558-0571. Tidskriftsartikel (refereegranskat)abstract Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2. Authors
45.	Ramos, Lucas P., et al. (författare) Robust Principal Component Analysis Techniques for Ground Scene Estimation in SAR Imagery 2023 Ingår i: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1939-1404 .- 2151-1535. ; 16, s. 9697-9710 Tidskriftsartikel (refereegranskat)abstract Robust principal component analysis (RPCA) has been widely used for processing and interpreting high-dimensional data in different applications such as data classification, face recognition, video analytics, and recommendation system design. However, the advancement of multisensor-based technologies and the emergence of large data sets have highlighted the limitations of traditional matrix-based models, which have paved the way for higher-order extensions such as tensor RPCA (TRPCA) techniques. These signal separation techniques can be useful for ground scene estimation (GSE) in synthetic aperture radar (SAR) imagery. GSE estimates the clutter-plus-noise content in the scene, and therefore, change detection (CD) methods can benefit, reducing the number of false alarms (FA). This paper presents two new GSE methods for SAR imagery based on robust PCA techniques. The first proposed method uses the RPCA via Principal Component Pursuit (PCP) to obtain the GSE-RPCA. The second method uses TRPCA via New Tensor Nuclear Norm (TNN) to obtain the GSE-TRPCA. The methodology allows the GSE to be obtained through a generalized regularization parameter. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve both optimization problems. Experimental results are evaluated considering real SAR imagery from two data sets acquired with the CARABAS II and ALOS PALSAR systems, respectively. Additionally, the proposed techniques were evaluated under several input characteristics, e.g., eight-image stacks and image pairs. Both GSE techniques are more robust to outliers and missing data when compared to existing solutions found in the literature. Finally, GSE-TRPCA achieved a minimum-square error performance of 0.0018 for some of the evaluated scenarios.
46.	Renato, Machado, et al. (författare) The stability of UWB low-frequency SAR images 2016 Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE. - 1545-598X .- 1558-0571. ; 13:8, s. 1114-1118 Tidskriftsartikel (refereegranskat)abstract This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system.
47.	Schlosser, Edson R., et al. (författare) Optimization of switched-beam arrays for communication systems 2014 Ingår i: 2014 11TH INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATIONS SYSTEMS (ISWCS). - : IEEE. - 9781479958634 ; , s. 579-583 Konferensbidrag (refereegranskat)abstract This paper presents the application of optimization methods for the synthesis of a linear array for communication systems. By means of suitably beam switching, the array should provide coverage of a given angular area in azimuth and should allow controlling the sidelobe level simultaneously. For this purpose, two optimization methods have been used to calculate the excitation coefficient for each desired beam. The synthesis technique is demonstrated for arrays composed of isotropic and microstrip elements. By comparing the results obtained for both arrays, the need of consideration of the array element pattern during the synthesis process is demonstrated.
48.	Schwartz, Christofer, et al. (författare) Change detection in UWB SAR images based on robust principal component analysis 2020 Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 12:12 Tidskriftsartikel (refereegranskat)abstract This paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method. © 2020 by the authors.
49.	ter Steege, Hans, et al. (författare) Mapping density, diversity and species-richness of the Amazon tree flora 2023 Ingår i: COMMUNICATIONS BIOLOGY. - 2399-3642. ; 6:1 Tidskriftsartikel (refereegranskat)abstract Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution. A study mapping the tree species richness in Amazonian forests shows that soil type exerts a strong effect on species richness, probably caused by the areas of these forest types. Cumulative water deficit, tree density and temperature seasonality affect species richness at a regional scale.
50.	Valduga, Samuel Tumelero, et al. (författare) Low-complexity codebook-based beamforming with four transmit antennas and quantized feedback channel 2014 Ingår i: 2014 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC). - Istanbul : IEEE. - 9781479930838 Konferensbidrag (refereegranskat)abstract In this paper we propose a low-complexity codebook-based beamforming with four transmit antennas and quantized feedback channel. The codebook design aggregates the effect of power allocation and phase rotation through a simple quantized transmit scheme. The codebook-based beamforming uses the feedback information in order to maximize the instantaneous signal-to-noise ratio (SNR) at the receiver. As a result, the proposed scheme presents an array gain. An SNR analysis is performed and it is used to find the optimal feedback information in the sense of maximizing the instantaneous SNR. A bit error rate (BER) analysis for a quantized feedback channel is also derived and it is used to compare to the results obtained for the proposed scheme under different levels of quantization. Simulations are performed over quasi-static flat Rayleigh fading channels for different closed-loop codebook-based schemes with four transmit antennas and unitary transmission rate. Results illustrate that the proposed scheme achieves full diversity order and outperforms other good schemes in terms of array gain.

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