Reconstruction Problem Research Articles

Joint Blind Reconstruction of the Cyclic Codes and Self-Synchronous Scramblers in a Noisy Environment

Reverse analysis of the intercepted signal to reconstruct the communication scheme used by the transmitter is a key problem in the non-cooperative context. In this paper, we propose a new algorithm for the joint blind reconstruction of the cyclic codes and self-synchronous scramblers directly using soft-decision sequences. First, we derive the conclusion that if the parity polynomial of the factor of the generator polynomial of the cyclic code and the feedback polynomial of the self-synchronous scrambler are multiplied, then the vector corresponding to the inverse polynomial of this product forms a parity-check relation with the row vector of the self-synchronously scrambled cyclic code bit matrix, which is constructed by the sequence of self-synchronously scrambled cyclic codes. To detect this parity-check relation, the concept of average parity-check probability (APCP) is defined, which uses the intercepted soft-decision sequence to measure the reliability of the parity-check relation. An optimal threshold based on the minimum error criterion is then derived. When the APCP is greater than this optimal threshold, the parity-check relation is considered to be detected, i.e., the corresponding irreducible polynomial is a factor of the generator polynomial of the cyclic code and the corresponding candidate feedback polynomial is the feedback polynomial of the self-synchronous scrambler. Therefore, the blind reconstruction problem in this paper is exactly equivalent to the hypothesis-testing problem. Simulation results show that the algorithm is more fault-tolerant than existing algorithms in noisy environments.

A Physics-Inspired Deep Learning Framework for an Efficient Fourier Ptychographic Microscopy Reconstruction under Low Overlap Conditions.

Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among them, Fourier Ptychographic Microscopy is of particular interest because of its important super-resolution factor. In complement to traditional intensity images, phase images are also produced. A large set of N raw images (with typically N = 225) is, however, required because of the reconstruction process that is involved. In this paper, we address the problem of FPM image reconstruction using a few raw images only (here, N = 37) as is highly desirable to increase microscope throughput. In contrast to previous approaches, we develop an algorithmic approach based on a physics-informed optimization deep neural network and statistical reconstruction learning. We demonstrate its efficiency with the help of simulations. The forward microscope image formation model is explicitly introduced in the deep neural network model to optimize its weights starting from an initialization that is based on statistical learning. The simulation results that are presented demonstrate the conceptual benefits of the approach. We show that high-quality images are effectively reconstructed without any appreciable resolution degradation. The learning step is also shown to be mandatory.

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.

Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae. Since the models' formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli. Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications.

A Comparison of Monocular Visual SLAM and Visual Odometry Methods Applied to 3D Reconstruction

Pure monocular 3D reconstruction is a complex problem that has attracted the research community’s interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera’s ego-motion; so, many methods have been proposed. However, most of these methods have not been evaluated on large datasets and under various motion patterns, have not been tested under the same metrics, and most of them have not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine-learning-based methods, and two updates of the best methods to identify the advantages and limitations of each category of the taxonomy and test whether the addition of machine learning or updates on those methods improved them significantly. Thus, we evaluated each algorithm using the TUM-Mono dataset and benchmark, and we performed an inferential statistical analysis to identify the significant differences through its metrics. The results determined that the sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly enhanced the geometric-based methods’ performance from different perspectives.

Wound Coverage Considerations for Leg Defects: Our Experience

Abstract Background: Lower limb injuries are common and account for 40-50% of emergency department visits. These injuries occur frequently in poly-trauma, which remains a leading cause of long hospitalization, disability and death for all ages. Clinical treatment of these injuries poses a great challenge because the surgeons need to face serious complications such as shock, wound coverage and infection, and has to choose a right time to deal with the combined fractures and injuries of the blood vessels, nerves and tendons. The aim of this study is to study clinical profile and management of various below knee soft tissue injuries. Methods: This study was conducted in two tertiary care centres of Northern India. Subjects were recruited from patients presenting in Emergency/Surgery OPD, a primary diagnosis of lower limb soft tissue injuries. Results: A total of 125 patients with lower limb injuries were included in the study. Different treatment options were individualized and exercised which included skin grafting (49.6%), flap coverage (44%) primary closure (4.8%) and skin substitutes like Integra (1.6%). While in 46 wounds (36.8%) negative pressure wound therapy (NPWT) VAC was used. It was observed that 92 patients (73.6%) underwent surgery only once whereas 33(26.4%) patients had to undergo surgery 2 or more times. Conclusion: The aim of surgical treatment in lower limb reconstructions is maximum limb functional recovery and return to work activities. Problems in reconstruction are mainly encountered in the ankle and lower third leg defects because, local tissue availability is very minimal in these areas.

Deep Fusion Network Based Sparse View CT Reconstructions for Clinical Diagnostic Scanners.

Sparse view CT scan has the advantage of reducing radiation exposure and scanning time in clinical diagnosis. However, the limited number of x-ray projections can make the reconstruction problem ill posed and result in image artifacts. To tackle the problem, we propose a novel model-based deep fusion network(DFN) satisfying the clinical set-up. It extracts fused features encoded from both the sinogram and the preliminary reconstructed image generated by filtered back projection (FBP) to improve the quality of reconstruction. The preliminary reconstructed image endows fused features with prior knowledge that facilitate the convergence of neural network to high-quality reconstruction images. We design a custom loss for training that enforces the network to learn both the pixel value and the integrity of the tissue structure. A synthetic sparse view breast CT dataset from American Association of Physicists in Medicine(AAPM) is used for training, validation and testing. The qualitative and quantitative evaluations show that the DFN reconstruction algorithm significantly improves in balancing between the image quality and reconstruction speed, hence enables fast and high quality CT reconstruction despite the sparse view limitations.

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma

Objective. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dose imposed by high-resolution micro cone-beam CT. Approach. A novel deep-learning approach is developed to enable BLT-based tumor targeting and treatment planning for orthotopic rat GBM models. The proposed framework is trained and validated on a set of realistic Monte Carlo simulations. Finally, the trained deep learning model is tested on a limited set of BLI measurements of real rat GBM models. Significance. Bioluminescence imaging (BLI) is a 2D non-invasive optical imaging modality geared toward preclinical cancer research. It can be used to monitor tumor growth in small animal tumor models effectively and without radiation burden. However, the current state-of-the-art does not allow accurate radiation treatment planning using BLI, hence limiting BLI’s value in preclinical radiobiology research. Results. The proposed solution can achieve sub-millimeter targeting accuracy on the simulated dataset, with a median dice similarity coefficient (DSC) of 61%. The provided BLT-based planning volume achieves a median encapsulation of more than 97% of the tumor while keeping the median geometrical brain coverage below 4.2%. For the real BLI measurements, the proposed solution provided median geometrical tumor coverage of 95% and a median DSC of 42%. Dose planning using a dedicated small animal treatment planning system indicated good BLT-based treatment planning accuracy compared to ground-truth CT-based planning, where dose-volume metrics for the tumor fall within the limit of agreement for more than 95% of cases. Conclusion. The combination of flexibility, accuracy, and speed of the deep learning solutions make them a viable option for the BLT reconstruction problem and can provide BLT-based tumor targeting for the rat GBM models.

Joint 2-D Sparse ISAR Imaging and Autofocusing by Using 2-D-IADIANet

Compressive sensing (CS) based methods have been widely used for sparse inverse synthetic aperture radar (ISAR) imaging. However, many CS-based methods are sensitive to the selection of model parameters, and the residual phase error of the echo also causes trouble for imaging and autofocusing. To address these problems, a novel deep learning approach, named as 2D-IADIANet, is proposed to achieve 2D sparse ISAR imaging with 2D phase error estimation in this paper. First, a 2D ISAR sparse echo model with 2D phase error into account is established, and a 2D-ADMM frame-work-based method, dubbed as 2D-IADIA, is presented to solve this compound reconstruction problem. Second, 2D-IADIA is further unfolded and mapped into a deep network form by integrating with 2D phase error compensation network. Moreover, all adjustable parameters can be learned adaptively by training the network through back propagation algorithm in complex domain directly. At last, experiment results verify that the well-learned 2D-IADIANet, which is only trained by a small amount of simulation samples, can also be generalized to measured data application. Especially, owing to the good performance of the network, the proposal has a superior reconstruction performance than 2D-IADIA under the low 2D sample rate and/or signal-to-noise ratio scenarios.

Robust ab initio solution of the cryo-EM reconstruction problem at low resolution with small data sets

Single particle cryo-electron microscopy has become a critical tool in structural biology over the last decade, able to achieve atomic scale resolution in three dimensional models from hundreds of thousands of (noisy) two-dimensional projection views of particles frozen at unknown orientations. This is accomplished by using a suite of software tools to (i) identify particles in large micrographs, (ii) obtain low-resolution reconstructions, (iii) refine those low-resolution structures, and (iv) finally match the obtained electron scattering density to the constituent atoms that make up the macromolecule or macromolecular complex of interest.Here, we focus on the second stage of the reconstruction pipeline: obtaining a low resolution model from picked particle images. Our goal is to create an algorithm that is capable of ab initio reconstruction from small data sets (on the order of a few thousand selected particles). More precisely, we propose an algorithm that is robust, automatic, and fast enough that it can potentially be used to assist in the assessment of particle quality as the data is being generated during the microscopy experiment.

Sparse reconstruction based on dictionary learning and group structure strategy for cone-beam X-ray luminescence computed tomography.

As a dual-modal imaging technology that has emerged in recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has exhibited promise as a tool for the early three-dimensional detection of tumors in small animals. However, due to the challenges imposed by the low absorption and high scattering of light in tissues, the CB-XLCT reconstruction problem is a severely ill-conditioned inverse problem, rendering it difficult to obtain satisfactory reconstruction results. In this study, a strategy that utilizes dictionary learning and group structure (DLGS) is proposed to achieve satisfactory CB-XLCT reconstruction performance. The group structure is employed to account for the clustering of nanophosphors in specific regions within the organism, which can enhance the interrelation of elements in the same group. Furthermore, the dictionary learning strategy is implemented to effectively capture sparse features. The performance of the proposed method was evaluated through numerical simulations and in vivo experiments. The experimental results demonstrate that the proposed method achieves superior reconstruction performance in terms of location accuracy, target shape, robustness, dual-source resolution, and in vivo practicability.

Multi-target reconstruction based on subspace decision optimization for bioluminescence tomography

Background and objectiveBioluminescence tomography (BLT) is a noninvasive optical imaging technique that provides qualitative and quantitative information on the spatial distribution of tumors in living animals. Researchers have proposed a list of algorithms and strategies for BLT reconstruction to improve its reconstruction quality. However, multi-target BLT reconstruction remains challenging in practical clinical applications due to the mutual interference of optical signals and difficulty in source separation. MethodsTo solve this problem, this study proposes the subspace decision optimization (SDO) approach based on the traditional iterative permissible region strategy. The SDO approach transforms a single permissible region into multiple subspaces by clustering analysis. These subspaces are shrunk based on subspace shrinking optimization to achieve spatial continuity of the permissible regions. In addition, these subspaces are merged to construct a new permissible region and then the next iteration of reconstruction is carried out to ensure the stability of the results. Finally, all the iterative results are optimized based on the normal distribution model and the distribution properties of the targets to ensure the sparsity of each target and the non-biasing of the overall results. ResultsExperimental results show that the SDO approach can automatically identify and separate different targets, ensuring the accuracy and quality of multi-target BLT reconstruction results. Meanwhile, SDO can combine various types of reconstruction algorithms and provide stable and high-quality reconstruction results independent of the algorithm parameters. ConclusionsThe SDO approach provides an integrated solution to the multi-target BLT reconstruction problem, realizing the whole process including target recognition, separation, reconstruction, and result enhancement, which can extend the application domain of BLT.

LDA enhanced one-bit compressive sensing method for high-throughput mass spectrometry data feature selection

Mass spectrometry (MS) is commonly used for the proteins detection of biological samples. However, it generates high-throughput data, posing huge computational challenges for signal processing. In previous studies, we have proposed a one-bit perturbed block CS (OB-PBCS) method to realize feature selection (FS) and obtain a feature set with reduced dimension for classification. However, its feature selection is realized by solving the reconstruction problem with simple greedy strategies, which fails to consider the final classification objective during reconstruction. Considering such problem, in this paper, we propose a hybrid linear discriminant analysis (LDA) enhanced one-bit CS method, called L-OBPBCS, which selects features by considering the subsequent classification and consists of three parts. The first part roughly filters out the features that do not favor the classification task with the proposed single feature decision coefficient. Then, OB-PBCS obtains significant features from the filtered feature set. Afterwards, LDA algorithm is further used for refining, where the amplitude of the optimal projection vector is gathered as the linear discrimination mapping coefficients to identify important features. To show the efficiency of our proposed L-OBPBCS method, classification based on the selected features for both synthesized dataset and real ovarian cancer MS dataset is evaluated. Results show that the feature set derived by L-OBPBCS ensures excellent classification performance, which surpasses the existing one-bit type FS method, and the computational complexity of L-OBPBCS is obviously reduced compared with the existing OB-PBCS method.

Distributed Network Reconstruction Based on Binary Compressed Sensing via ADMM

At present, network model is a general framework for the representation of complex system, and its structure is the fundamental and prerequisite for control and other applications of networked system. Due to the advent of Big Data era, the network structure scale is expanding sharply. Obviously, the traditional centralized reconstruction methods require high-performance computing resources and can hardly be suitable in practice. Therefore, it is a challenge to reconstruct large-scale networks with limited resources. To resolve the problem, a distributed local reconstruction method is proposed for unweighted networks. Specifically, the local reconstruction problems of nodes are distributed to multiple computing units. ADMM is introduced for compressed sensing framework to decompose the complex reconstruction problem into multiple subproblems, so it can reduce the high requirement of computing resources. Through parallel computing, network reconstruction subproblems are solved simultaneously. In addition, to further guarantee the reconstruction accuracy, a binary constraint is introduced based on the characteristics obtained by analyzing the network structure. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed method. Compared with some state-of-the-art methods, the proposed method can reconstruct networks of different scales and types with limited computing resources, and it is accurate and robust against noise.

Insertion and Deletion Correction in Polymer-Based Data Storage

Synthetic polymer-based data storage seems to be a particularly promising candidate that could help to cope with the ever-increasing demand for archival storage requirements. It involves designing molecules of distinct masses to represent the respective bits {0, 1}, followed by the synthesis of a polymer of molecular units that reflects the order of bits in the information string. Reading out the stored data requires the use of a tandem mass spectrometer, that fragments the polymer into shorter substrings and provides their corresponding masses, from which the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">composition</i> , i.e. the number of 1s and 0s in the concerned substring can be inferred. Prior works have dealt with the problem of unique string reconstruction from the set of all possible compositions, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">composition multiset</i> . This was accomplished either by determining which string lengths always allow unique reconstruction, or by formulating coding constraints to facilitate the same for all string lengths. Additionally, error-correcting schemes to deal with substitution errors caused by imprecise fragmentation during the readout process, have also been suggested. This work builds on this research by extending previously considered error models, mainly confined to substitution of compositions. To this end, we define new error models that consider insertions of spurious compositions and deletions of existing ones, thereby corrupting the composition multiset. We analyze if the reconstruction codebook proposed by Pattabiraman <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . is indeed robust to such errors, and if not, propose new coding constraints to remedy this.

Effective Electrical Impedance Tomography Based on Enhanced Encoder–Decoder Using Atrous Spatial Pyramid Pooling Module

Electrical impedance tomography (EIT) is a noninvasive and radiation-free imaging method. As a "soft-field" imaging technique, in EIT, the target signal in the center of the measured field is frequently swamped by the target signal at the edge, which restricts its further application. To alleviate this problem, this study presents an enhanced encoder-decoder (EED) method with an atrous spatial pyramid pooling (ASPP) module. The proposed method enhances the ability to detect central weak targets by constructing an ASPP module that integrates multiscale information in the encoder. The multilevel semantic features are fused in the decoder to improve the boundary reconstruction accuracy of the center target. The average absolute error of the imaging results by the EED method reduced by 82.0%, 83.6%, and 36.5% in simulation experiments and 83.0%, 83.2%, and 36.1% in physical experiments compared with the errors of the damped least-squares algorithm, Kalman filtering method, and U-Net-based imaging method, respectively. The average structural similarity improved by 37.3%, 42.9%, and 3.6%, and 39.2%, 45.2%, and 3.8% in the simulation and physical experiments, respectively. The proposed method provides a practical and reliable means of extending the application of EIT by solving the problem of weak central target reconstruction under the effect of strong edge targets in EIT.

Test Methods for Signal Reconstruction of Linear Stationary Systems

The test methods designed for mathematical reconstruction of input signals of linear stationary information processing systems from distorted and noisy output signals are proposed and analyzed. It is shown that the use of test data allows one to carry out a non-blind reconstruction without determining the hardware functions of processing systems, which, in general, can belong to the class of generalized functions. The features of the use of the regularization technique in test methods are considered when solving ill-posed and ill-conditioned problems of reconstruction of real non-deterministic signals. The criteria for selecting test signals are analyzed. The results of numerical experiments on the restoration of one-dimensional signals and two-dimensional images at different noise levels are presented.

Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation.

We present a potent computational method for the solution of inverse problems in fluid mechanics. We consider inverse problems formulated in terms of a deterministic loss function that can accommodate data and regularization terms. We introduce a multigrid decomposition technique that accelerates the convergence of gradient-based methods for optimization problems with parameters on a grid. We incorporate this multigrid technique to the Optimizing a DIscrete Loss (ODIL) framework. The multiresolution ODIL (mODIL) accelerates by an order of magnitude the original formalism and improves the avoidance of local minima. Moreover, mODIL accommodates the use of automatic differentiation for calculating the gradients of the loss function, thus facilitating the implementation of the framework. We demonstrate the capabilities of mODIL on a variety of inverse and flow reconstruction problems: solution reconstruction for the Burgers equation, inferring conductivity from temperature measurements, and inferring the body shape from wake velocity measurements in three dimensions. We also provide a comparative study with the related, popular Physics-Informed Neural Networks (PINNs) method. We demonstrate that mODIL has three to five orders of magnitude lower computational cost than PINNs in benchmark problems including simple PDEs and lid-driven cavity problems. Our results suggest that mODIL is a very potent, fast and consistent method for solving inverse problems in fluid mechanics.

Sequence Reconstruction Under Single-Burst-Insertion/Deletion/Edit Channel

Motivated by applications in modern storage devices such as DNA storage and racetrack memories, we study the sequence reconstruction problem which involves two important issues. One is determining the maximum intersection size between two error balls. The other is designing reconstruction codes in which each transmitted sequence can be reconstructed from a given number of noisy reads with redundancy as small as possible. In this paper, we focus on channels that introduce single-burst-insertion/ deletion error. We fully determine the maximum intersection size between two burst-insertion/deletion balls for both fixed-length and variable-length models. Then we characterize a pair of sequences when their burst-insertion/deletion balls have intersection of a certain size. Using these characterizations, we design reconstruction codes for all values <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> of the number of noisy reads and analyze their lower bounds. In addition, we extend our results to the burst-edit channel.

Compressive frequency-difference direction-of-arrival estimation

Direction-of-arrival estimation is difficult for signals spatially undersampled by more than half the wavelength. Frequency-difference beamforming [Abadi, Song, and Dowling (2012). J. Acoust. Soc. Am. 132, 3018–3029] offers an alternative approach to avoid such spatial aliasing by using multifrequency signals and processing them at a lower frequency, the difference-frequency. As with the conventional beamforming method, lowering the processing frequency sacrifices spatial resolution due to a beam broadening. Thus, unconventional beamforming is detrimental to the ability to distinguish between closely spaced targets. To overcome spatial resolution deterioration, we propose a simple yet effective method by formulating the frequency-difference beamforming as a sparse signal reconstruction problem. Similar to compressive beamforming, the improvement (compressive frequency-difference beamforming) promotes sparse nonzero elements to obtain a sharp estimate of the spatial direction-of-arrival spectrum. Analysis of the resolution limit demonstrates that the proposed method outperforms the conventional frequency-difference beamforming in terms of separation if the signal-to-noise ratio exceeds 4 dB. Ocean data from the FAF06 experiment support the validity.

Societal Consequences of Modern Hybrid Warfare in Political-Economic and Institutional Discourse

Object: to reveal the features of the societal approach to the study of the modern hybrid war consequences, a striking example of which is the Russian invasion of Ukraine in February 2022, based on the study of the reflection of changes in social reality in the unity of political-economic, sociological and institutional discourses.&#x0D; Methods: ascension from the abstract to the concrete; dialectical; logical-historical; fractal method of institutional economics; structural-functional and world-systems analysis. Findings: the current Russian-Ukrainian war has clear signs of a modern hybrid war generated by the globallocal hybrid system “peace — war” and combines a whole complex of means: military-political, economic, cybernetic, etc. On the basis of the combination of political-economic, sociological (in a broad sense) and institutional discourses, it is substantiated that the main components of the “peace — war” hybrid system from the point of view of the societal relations transformation are: armed-military, financial-economic and informational-spiritual components. It is shown that through the channels of world perception and positioning they lead to changes in behavioural patterns, through which the response of the actors is carried out, changing the already changing reality. This leads to the closure of changes in social reality through the behaviour of subjects on themselves, gives integrity and acceleration to societal processes.&#x0D; Conclusions: the feature of the societal approach is revealed only on the basis of a combination of politicaleconomic (which opens up the possibility of tracing chains of cause-and-effect relationships), sociological in a broad sense (allowing you to look at society as a whole in the unity of its components and measure them) and institutional (giving the key to understanding changes in rules and norms of social life) approaches. This feature consists, in contrast to the social approach (fixing the structural and functional structure of social reality), in the study of social transformations, which actualizes the problems of interaction and reflexivity of social structures and functions. In the conditions of martial law, the role of the trust institution as a defining and integral construct of the socio-economic system’s institutional architectonics, embodied both in trust in the state and power structures, and in trust in new institutions born of hybrid war. The societal approach opens up the possibility of a new look at the problems of the Ukrainian post-war reconstruction, taking into account both the objective logic of social transformations and the need to increase social activity in institutional transformations that involve the formation of a new development model in the European socio-economic space.