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Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Постановка проблеми.Актуальність теми реконструювання пам'яток архітектури очевидна. Досить зазначити, що цілі міста відновлювалися після світових війн у первозданному вигляді. Для складання проектів реставрації використовуються різні джерела, в тому числі й архівні фотознімки. Тому нові методи метричної обробки фотознімків мають велике значення. Загального методу реконструювання фотознімків не може бути, тому що це залежить від кількості знімків, форми зображених об'єктів, безлічі інших умов. Крайній випадок – реконструювання з одиночного фотознімка. Одне зображення без додаткових умов не визначає ні метричних, ні позиційних властивостей простору. Однак залежно від останніх можна визначити форму, розміри, дані про проекційний апарат і їх взаємне розташування. Для реконструювання знімків за обрисами проекцій поверхонь обертання використовувалося зображення одиночної поверхні, а відсутня інформація поповнювалася з поєднань різних умов іншого роду, накладених на фотознімок об'єкта. Мета дослідження–розробити метод геометричного реконструювання за фотознімком об'єкта, що містить ряд поверхонь обертання, які не перекривають одна одну, заснований на використанні координат точок на обрисах проекцій поверхонь, що дозволить розкрити «метод обрисів». Висновок.Запропонований метод реконструювання фотознімків дозволяє максимально використовувати інформацію, що міститься на них, і в поєднанні з іншими методами з'ясувати значення шуканих величин і оцінити їх точність. Метрична обробка фотознімків дає можливість виключити етап макетування, що здешевлює складання проектів реставрації., Постановка проблемы. Актуальность темы реконструкции памятников архитектуры очевидна. Достаточно отметить, что целые города восстанавливались после мировых войн в первозданном виде. Для составления проектов реставрации используются различные источники, в том числе и архивные фотоснимки. Поэтому новые методы метрической обработки фотоснимков имеют большое значение. Общего метода реконструирования фотоснимков не может быть, так как это зависит от количества снимков, формы изображенных объектов, множества других условий. Крайний случай – реконструирование по одиночному фотоснимку. Одно изображение без дополнительных условий не определяет ни метрических, ни позиционных свойств пространства. Однако в зависимости от последних можно определить форму, размеры, данные о проецирующем аппарате и их взаимном расположении. Для реконструирования снимков по очеркам проекций поверхностей вращения использовалось изображение одиночной поверхности, а недостающая информация восполнялась из сочетаний различных условий другого рода, наложенных на фотоснимок объекта. Цель исследования – разработать метод геометрического реконструирования по фотоснимку объекта, содержащего ряд поверхностей вращения, не перекрывающих друг друга, основанный на использовании координат точек на очерках проекций поверхностей, что позволит раскрыть «метод очерков». Вывод.Предложенный метод реконструирования фотоснимков позволяет максимально использовать содержащуюся на них информацию и в сочетании с другими методами определить значения искомых величин и оценить их точность. Метрическая обработка фотоснимков дает возможность исключить этап макетирования, что удешевляет составление проектов реставрации., Problem statement. The relevance of the reconstruction of architectural monuments is obvious. It is enough to note that entire cities were restored after the world wars in their original form. For the preparation of restoration projects, various sources are used, including archival photographs. Therefore, new methods of metric processing of photographs are important. There can be no general method for reconstruction of photographs, since it depends on the number of pictures, the shape of the depicted objects, and many other conditions. The special case is reconstructions from a single photograph. A single image without additional conditions determines neither metric nor positional properties of space. However, depending on the latter, you can determine the shape, size, data about the projecting apparatus and their relative position., An image of a single surface was used, to reconstruct images from sketches of projections of surfaces of revolution and the missing information was made up from combinations of various conditions of a different kind superimposed on the photograph of the object. Purpose.To develop a method of geometric reconstruction from a photograph of an object containing a number of surfaces of revolution that do not overlap, based on the use of the coordinates of points on the sketches of the projections of the surfaces, which will allow to reveal the "sketch method". Conclusion. The proposed method for reconstruction of photographs allows using in maximum the information contained on them and, in combination with other methods, makes it possible to determine the values of the desired values and evaluate their accuracy. Metric processing of photographs makes it possible to eliminate the stage of prototyping for reduction of the cost of projects restoration.

Computed tomography (CT) has been widely used for long. In recently years, the useful application of CT would be the method of the reconstruction images. Historically speaking, reconstruction images were conducted so far by algebraic reconstruction technique (ART), filtered back projection (FBP), iterative reconstruction (IR) algorithms and so on. There are various effects of reconstruction image of CT in clinical practice. Two cases with such beneficial experiences were reported. Case 1 is 74-year-man with hypertension. He was diagnosed to have abdominal aortic aneurysm (AAA). By reconstruction images, the general shape and details were clearly observed including regulating background data. Case 2 is 48-year-female without history of low back pain. When she received abdominal CT, she was pointed out to have lumbar disc herniation between L4 and L5. The diagnosis was done from the lateral tomography of lumbar vertebrae. By reconstruction images with colored modification, the detail status of herniation was clearly observed. Consequently, reconstruction image method of CT would be useful and effective. Further evolution of CT apparatus and development of computer analysis would be expected for better clinical practice and research in the future.

Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers., QC 20201204

Background Metabolic bone disease after gastrectomy is one of the complications leading to deterioration in quality of life. The exact mechanism of the metabolic bone disease remains unclear. To clarify the cause of metabolic bone disease after gastrectomy, we evaluated the associations between the method of gastrectomy and the development of metabolic bone disease in a rat model. Methods Rats were assigned to four groups as follows: (1) sham operation (control group); (2) resection of the glandular stomach with Billroth I reconstruction (RGBI group); (3) Roux-en-Y anastomosis preserving the secretory function of the whole stomach (PSRY group); and (4) total gastrectomy with Roux-en-Y reconstruction (TGRY group). In all groups, body weight, serum biochemistry (total protein, albumin, calcium, phosphorus, tartrate-resistant acid phosphatase, and bone alkaline phosphatase), bone density, and bone breaking strength were measured. Results Body weights and serum calcium levels were significantly lower in the three operation groups compared with the control group. Bone density was significantly lower in the PSRY and TGRY groups compared with the control group. Bone breaking strength was significantly lower in the three operation groups compared with the control group. Conclusions Surgical methods led to metabolic bone disease. However, exclusion of the duodenum from food passage had major influence to reduction in bone density and breaking strength. A stomach-preserving procedure and physiological reconstruction which enable food passage through duodenum and proximal jejunum contribute to mitigation of metabolic bone disease. © 2016 Elsevier Inc., Embargo Period 12 months

Diffusion magnetic resonance imaging (dMRI) has had a great impact on the study of the human brain connectome. Tractography methods allow for the reconstruction of white matter fiber tracts and bundles across the brain, by tracing the estimated direction of water diffusion across neighboring voxels. The tracts can then be used in conjunction with cortical parcellations to create structural connectivity matrices, to map the pattern and distribution of connections between cortical regions. However, the reliability of connectivity matrices is unclear. Tractography results depend on image resolution, and some reconstruction methods used to resolve the voxel-wise microstructure may be more robust to changes in resolution than others, leading to more stable connectivity estimates. We examined the reliability of structural connectivity matrices in 20 healthy young adults imaged with both high and low-resolution dMRI at two time points. We found that the Constrained Spherical Deconvolution (CSD) model produces the most reliable connections for both lower resolution and high resolution scans. © Springer International Publishing Switzerland 2016.

We identify two issues with the reverse regression approach as implemented in several classic reconstructions of past climate fluctuations from dendroclimatologcical data series. First, instead of estimating the causal relationship between the proxy, which is measured with significant error, as function of climate and formally inverting the relationship, most papers estimate the inverted relationship directly. This leads to biased coefficients and reconstructions with artificially low variance. Second, we show that inversion of the relationship is often done incorrectly when the underlying causal relationship is dynamic in nature. We show analytically as well as using Monte Carlo experiments and actual tree ring data, that the reverse regression method results in biased coefficients, reconstructions with artificially low variance and overly smooth reconstructions. We further demonstrate that correct application of the inverse regression method is preferred. However, if the measurement error in the tree ring index is significant, neither method provides reliable reconstructions.

We identify two issues with the reverse regression approach as implemented in several classic reconstructions of past climate fluctuations from dendroclimatologcical data series. First, instead of estimating the causal relationship between the proxy, which is measured with significant error, as function of climate and formally inverting the relationship, most papers estimate the inverted relationship directly. This leads to biased coefficients and reconstructions with artificially low variance. Second, we show that inversion of the relationship is often done incorrectly when the underlying causal relationship is dynamic in nature. We show analytically as well as using Monte Carlo experiments and actual tree ring data, that the reverse regression method results in biased coefficients, reconstructions with artificially low variance and overly smooth reconstructions. We further demonstrate that correct application of the inverse regression method is preferred. However, if the measurement error in the tree ring index is significant, neither method provides reliable reconstructions.

In this paper, a method that performs 3D object reconstruction from multiple views of the same scene is presented. This reconstruction method initially produces a basic model, based on the space carving algorithm, that is further refined in a subsequent step. The algorithm is fast, computationally simple and produces accurate representations of the input scenes. In addition, compared to previously presented works the proposed algorithm is able to cope with non uniformly lighted scenes due to the characteristics of the used voxel dissimilarity measure. The proposed algorithm is assessed and the experimental results are presented and discussed., QC 20111213

Reconstruction of magnetic resonance images from data not falling on a Cartesian grid is widely used for fast acquisitions, and it is a Fourier inversion problem typically solved using convolution interpolation, also known as gridding. This work presents a comparison between two gridding reconstruction methods to reconstruct magnetic resonance images from acquisitions using spiral trajectories through k-space. One method (grid-driven) is not based on a density compensation function while the other one (Direct Summation) uses Voronoi cells for the determination of the necessary areas to estimate the corresponding density compensation function. Both methods have been applied to the same image to see the reconstruction quality of each method. Both methods have correctly reconstructed the original image using only 13.73% of the original full-grid data from a Cartesian trajectory. © 2011 IEEE.

Reconstruction of magnetic resonance images from data not falling on a Cartesian grid is widely used for fast acquisitions, and it is a Fourier inversion problem typically solved using convolution interpolation, also known as gridding. This work presents a comparison between two gridding reconstruction methods to reconstruct magnetic resonance images from acquisitions using spiral trajectories through k-space. One method (grid-driven) is not based on a density compensation function while the other one (Direct Summation) uses Voronoi cells for the determination of the necessary areas to estimate the corresponding density compensation function. Both methods have been applied to the same image to see the reconstruction quality of each method. Both methods have correctly reconstructed the original image using only 13.73% of the original full-grid data from a Cartesian trajectory. © 2011 IEEE.

Diffusion tensor imaging (DTI) is a popular magnetic resonance imaging technique that provides in vivo information about tissue microstructure, based on the local water diffusion environment. DTI models the diffusion displacement of water molecules in tissue as a Gaussian distribution. In this dissertation, to mimic the complex nature of water diffusion in brain tissues, a diffusion kurtosis model is used, to incorporate important non-Gaussian diffusion properties. This diffusion kurtosis imaging (DKI) is applied in an experimental traumatic brain injury in a rat model, to study whether it provides more information on microstructural changes than standard DTI. Our results indicate changes in ordinary DTI parameters, in various brain regions following injury, normalize to the baseline by the sub-acute stage. However, DKI parameters continue to show abnormalities at this sub-acute stage, as confirmed by immunohistochemical examination. Specifically, increased mean kurtosis (MK) was found to associate with increased reactive astrogliosis, a hallmark for inflammation, even in regions far removed from the injury foci. Findings suggest that monitoring changes in MK enhances the investigation of molecular and morphological changes in vivo. Extending DKI to clinical usage, however, poses several challenges: (a) long image acquisition time (~20 min) due to the augmented measurements required to fit the more complex model, (b) slow image reconstruction (~90 min) due to required nonlinear fitting and, (c) errors associated with fitting the inherently low signal-to-noise ratio (SNR) images from higher diffusion weighting. The second portion of this dissertation is devoted to developing imaging schemes and image reconstruction methods that facilitate clinical DKI applications. A fast and efficient DKI reconstruction method is developed with a reconstruction time of 2-3 seconds, with improved accuracy and reduced variability in DKI estimation over conventional methods. Further an

The marine boundary layer (MBL) is the region where energy, momentum and masses are exchanged between the ocean surface and the free troposphere. The lack of observations with high vertical resolution over the ocean significantly restricts the understanding of the complex physical processes that occur inside the MBL. The relatively short vertical extent of the MBL (average about 1~2 km) and the frequent cloudiness at its top make probing the MBL extremely difficult from the space. Several features of the Global Positioning System (GPS) radio occultation (RO) technique suggest that it has a great potential for sensing the MBL. These features include global coverage, high vertical resolution, and the ability of GPS signals to penetrate clouds.Over moist marine areas, a large negative moisture gradient often exists across the thermal inversion capping the MBL, which can cause super-refraction (SR) or ducting. A large number of high-resolution soundings have shown that SR occurs about 90% of the time in a year over the subtropical and tropical oceans and even 50% at high-latitudes during the summer. In the presence of SR, the reconstruction of refractivity from RO data becomes an ill-posed inverse problem, i.e., a given RO bending angle profile is consistent with a continuum (an infinite number) of refractivity profiles. The standard Abel retrieval gives the minimum refractivity solution of the continuum and thus produces the largest negative bias, consistent with a negative bias that is often present in the retrieved refractivity profiles in the moist lower troposphere. Simulation studies indicate a large variation of the negative refractivity biases (could be over -15%). The impact of diffraction effects and the open-loop receiver tracking on the bending angle and refractivity retrievals are assessed. A novel approach is developed and tested to reconstruct the vertical refractivity structure within and below the SR layer, which yields a much-improved retrieval, especi

The fundamental issue of determining acoustic properties of porous media from their local geometry is examined in this PhD dissertation thesis, thanks to a sample of open-cell aluminum foam analyzed by axial computed microtomography. Various geometric properties are measured to characterize the experimental sample at the cell size level. This is done in order to reconstruct a porous medium by means of idealized three- and two- dimensional unit-cells.The frequency dependant thermal and velocity fields governing the propagation and dissipation of acoustic waves through rigid porous media are computed by Brownian motion simulation and the finite element method, respectively. Macroscopic behavior is derived by spatial averaging of the local fields. Our results are compared to experimental data obtained from impedance tube measurements. Firstly, this approach leads to the identification of the macroscopic parameters involved in Pride and Lafarge semiphenomenological models. Secondly, it yields a direct access to thermal and viscous dynamic permeabilities. However, the bi-dimensional model underestimates the static viscous permeability as well as the viscous characteristic length; what thus require a three-dimensional implementation.