Recovery Error Research Articles

Nanocomposite modified electrode based on polyaniline/Ti3C2 for the simultaneous detection of purine bases and uric acid

In this study, a nanocomposite of Polyaniline/titanium carbide (PANI/Ti3C2) was synthesized through self-assembly and in situ polymerization for use as a sensor material to simultaneously detect guanine (G), adenine (A), and uric acid (UA). X-ray diffraction (XRD), Fourier transform infrared spectrometer (FTIR), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) were utilized to confirm the successful synthesis of PANI/Ti3C2. Electrochemical analysis methods including differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) were used to detect the electrochemical performance of PANI/Ti3C2 modified glassy carbon electrode (PANI/Ti3C2/GCE). The pH dependence, electrochemically active surface area (ECSA), electron transport, stability, reproducibility, sensitivity, and selectivity of the modified GCE were examined. Compared to previous reports, the nanocomposite exhibited excellent performance. Uric acid, guanine, and adenine detection limits were 0.33, 0.31, and 0.28 μM, respectively, over concentration ranges of 0.62 - 20.10, 0.31 - 17.00, and 0.31 - 17.00 μM. The nanocomposite demonstrated high recovery and low error in serum sample detection. In summary, PANI/Ti3C2 is a sensor material with excellent electrochemical properties.

FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition.

Efficient and accurate BRDF acquisition of real world materials is a challenging research problem that requires sampling millions of incident light and viewing directions. To accelerate the acquisition process, one needs to find a minimal set of sampling directions such that the recovery of the full BRDF is accurate and robust given such samples. In this article, we formulate BRDF acquisition as a compressed sensing problem, where the sensing operator is one that performs sub-sampling of the BRDF signal according to a set of optimal sample directions. To solve this problem, we propose the Fast and Robust Optimal Sampling Technique (FROST) for designing a provably optimal sub-sampling operator that places light-view samples such that the recovery error is minimized. FROST casts the problem of designing an optimal sub-sampling operator for compressed sensing into a sparse representation formulation under the Multiple Measurement Vector (MMV) signal model. The proposed reformulation is exact, i.e. without any approximations, hence it converts an intractable combinatorial problem into one that can be solved with standard optimization techniques. As a result, FROST is accompanied by strong theoretical guarantees from the field of compressed sensing. We perform a thorough analysis of FROST-BRDF using a 10-fold cross-validation with publicly available BRDF datasets and show significant advantages compared to the state-of-the-art with respect to reconstruction quality. Finally, FROST is simple, both conceptually and in terms of implementation, it produces consistent results at each run, and it is at least two orders of magnitude faster than the prior art.

Deblending overlapping galaxies in DECaLS using transformer-based algorithm: A method combining multiple bands and data types

Abstract In large-scale galaxy surveys, particularly deep ground-based photometric studies, galaxy blending was inevitable. Such blending posed a potential primary systematic uncertainty for upcoming surveys. Current deblenders predominantly depended on analytical modelling of galaxy profiles, facing limitations due to inflexible and imprecise models. We presented a novel approach, using a U-net structured transformer-based network for deblending astronomical images, which we term the CAT-deblender. It was trained using both RGB and the grz-band images, spanning two distinct data formats present in the Dark Energy Camera Legacy Survey (DECaLS) database, including galaxies with diverse morphologies in the training dataset. Our method necessitated only the approximate central coordinates of each target galaxy, sourced from galaxy detection, bypassing assumptions on neighbouring source counts. Post-deblending, our RGB images retained a high signal-to-noise peak, consistently showing superior structural similarity against ground truth. For multi-band images, the ellipticity of central galaxies and median reconstruction error for r-band consistently lie within $\pm$ 0.025 to $\pm$ 0.25, revealing minimal pixel residuals. In our comparison of deblending capabilities focused on flux recovery, our model showed a mere 1% error in magnitude recovery for quadruply blended galaxies, significantly outperforming SExtractor’s higher error rate of 4.8%. Furthermore, by cross-matching with the publicly accessible overlapping galaxy catalogs from the DECaLS database, we successfully deblended 433 overlapping galaxies. Moreover, we have demonstrated effective deblending of 63 733 blended galaxy images, randomly chosen from the DECaLS database.

Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection.

Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data. Concretely, we propose a novel two-stream framework based on context recovery and knowledge retrieval, where the two streams can complement each other. For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame. Furthermore, we propose a maximum local error mechanism to alleviate the problem of large recovery errors caused by complex foreground objects. For the knowledge retrieval stream, we propose an improved learnable locality-sensitive hashing, which optimizes hash functions via a Siamese network and a mutual difference loss. The knowledge about normality is encoded and stored in hash tables, and the distance between the testing event and the knowledge representation is used to reveal the probability of anomaly. Finally, we fuse the anomaly scores from the two streams to detect anomalies. Extensive experiments demonstrate the effectiveness and complementarity of the two streams, whereby the proposed two-stream framework achieves state-of-the-art performance on ShanghaiTech, Avenue and Corridor datasets among the methods without object detection. Even if compared with the methods using object detection, our method reaches competitive or better performance on the ShanghaiTech, Avenue, and Ped2 datasets.

Data-Based Tampered-Data Recovery Strategy With Encoding Against Stealthy Attack for Unknown Discrete-Time Systems.

This study addresses a tampered-data recovery problem for linear discrete-time systems with completely unknown system dynamics under stealthy attacks. The basic idea is to identify the stealthy attack, that lies in any of attack-stealthy subspaces, and compensate for it. Different from the existing sparse recovery methods which are applicable to nonstealthy sparse attacks, a novel encoding scheme, where a set of subdecoding matrices is designed specifically for each 1-D attack-stealthy subspace, is developed so that the parameters of the stealthy attack can be identified via a subspace projection technique. A necessary and sufficient condition of determining the targeted subspace by using n parallel attack identification filters is established for this encoding scheme. Especially, a composite encoding matrix characterizes the lower and upper boundaries of the recovery error covariance's trace. A simulation example of a flight vehicle illustrates the efficiency of the proposed approach.

Compressing the Geospatial Data of Testing Grounds

The paper discusses an algorithm for obtaining the compressed spatial images of testing grounds, which are described by attribute data. The attributive representations of data make it possible to use the hardware and software resources of geographical information systems (GIS) more economically. The objects, that can be presented by a boundary, are considered as the observable ones. The efficiency of the testing ground description is achieved by representing all its areas by boundaries, encoding the boundaries using the Freeman code, and applying the entropy encoding at the last stage of image processing. At the same time, the compressed set of attributive data can be unpacked into the original image with almost no recovery errors or with acceptable accuracy, but not below the limit set by the specification. The theoretical principles of the method are illustrated by the example of processing the segmented image of the contour. The comparative assessment of the efficiency of compression of the combined method under consideration and that of the entropy encoding method is presented.

Two novel models with nuclear norm for robust matrix recovery

In this paper, we investigate the low-rank matrix recovery problem from linear observations. Inspired by the matrix Lasso and Dantzig selector, we propose nuclear norm regularized models with two data fitting items: ℓ2-loss function and Dantzig selector. We establish the recovery errors for both constrained and unconstrained models under the matrix restricted isometry property frame. For the unconstrained model, we obtain a tighter error bound compared with the previous study by W. Wang et al. (2021) under certain conditions. Furthermore, we design an effective solving algorithm for the proposed unconstrained model. Our numerical experiments demonstrate that our method outperforms the Lasso and Dantzig selector models.

Application of the liquid-liquid extraction method and subsequent icp-ms detection for speciation analysis of arsenic in wastewater samples

The current paper describes a method for determining As(III) and As(V) concentrations in wastewater samples. The approach is based on the extraction of As(III) from water samples using a non-polar organic solvent in a 10-12 M HCl medium. The extract contains As(III), while the raffinate contains As(V). Distilled water can be used to re-extract As(III) from the organic solvent. ICP-MS was used to analyze As concentrations in the reextract and the raffinate. The average recovery of arsenic by the proposed method is 99.6 %. It was calculated as the sum of As(III) and As(V) concentrations. The collected data demonstrated high repeatability. The standard addition procedure demonstrated the selectivity and accuracy of the employed method. The relative error of recovery of the standard As(III) addition is about 2 %, which is similar to the RSD found under repeatability conditions.

Energy-efficient algorithms for lossless data compression schemes in wireless sensor networks

Wireless sensor networks (WSNs) are reliant on limited power resources, primarily provided by small batteries within sensor nodes. Inefficient energy management within these networks can lead to premature battery depletion during data transmission between sensor nodes, significantly impacting network longevity. Data compression emerges as a viable strategy to mitigate energy consumption by reducing data size before transmission and employing various compression and decompression techniques. This work presents a comparative analysis of data compression algorithms tailored for WSNs. It studies and enhances two adaptive lossless data compression techniques, namely ‘Adaptive Lossless Data Compression’ (ALDC) and ‘Fast and Efficient, Lossless Adaptive Compression System’ (FELACS), as means to effectively manage energy consumption in wireless sensor networks. ALDC and FELACS algorithms encode differences between consecutive data readings, thereby reducing the number of bits required for encoding. ALDC employs Huffman coding, while FELACS leverages the Golomb-Rice coding method. Encoding data samples by using three Huffman tables interchangeably as an enhancement of the ALDC algorithm, resulted in an improvement in energy saving from 73 % to 77 %. Analysis of FELACS unveiled the impact of natural phenomena-induced anomalies on measured data, identified as outliers. The outliers disrupt data patterns and ranges, subsequently altering the optimal coding parameters for data samples, resulting in encoding and decoding errors. This study proposes a robust method for identifying and replacing outliers within sensor data, significantly enhancing compression performance. A reduction of variations in dataset patterns facilitated more accurate sampling and encoding of data. Consequently, fewer bits are required to encode data samples, rendering the algorithm energy-efficient and suitable for applications demanding error-free data recovery or meticulous error analysis. The proposed method was successfully applied to the modified ALDC algorithm, exhibiting efficient performance. An optimum block size of sampled data was discovered for Fishnet relative humidity deployment ensuring efficient transmission of environmental data real-world sensor network deployments like Fishnet, Lucerne, and Le Genepi. These findings underscore the potential for significant energy savings and improved data accuracy through adaptive lossless data compression techniques, making them valuable assets for applications with stringent energy constraints or demanding data integrity.

Semi-tensor product-based one-bit compressed sensing

The area of one-bit compressed sensing (1-bit CS) focuses on the recovery of sparse signals from binary measurements. Over the past decade, this field has witnessed the emergence of well-developed theories. However, most of the existing literature is confined to fully random measurement matrices, like random Gaussian and random sub-Gaussian measurements. This limitation often results in high generation and storage costs. This paper aims to apply semi-tensor product-based measurements to 1-bit CS. By utilizing the semi-tensor product, this proposed method can compress high-dimensional signals using lower-dimensional measurement matrices, thereby reducing the cost of generating and storing fully random measurement matrices. We propose a regularized model for this problem that has a closed-form solution. Theoretically, we demonstrate that the solution provides an approximate estimate of the underlying signal with upper bounds on recovery error. Empirically, we conduct a series of experiments on both synthetic and real-world data to demonstrate the proposed method’s ability to utilize a lower-dimensional measurement matrix for signal compression and reconstruction with enhanced flexibility, resulting in improved recovery accuracy.

Flow enhancement from wall pressure observations: A compressible continuous adjoint data assimilation model

This study establishes a compressible continuous adjoint data assimilation (C2ADA) approach for reproducing a complete mean flow from sparse wall pressure observations. The model-form error induced by the Boussinesq approximation is corrected by the addition of a spatially varying additive forcing term. The linear part of the eddy viscosity, computed using the conventional Reynolds-averaged Navier–Stokes model, is incorporated for ensuring the well-posedness of the optimization. The model is derived theoretically to minimize discrepancies between the wall pressure measurements and the numerical predictions of the primary-adjoint system, thereby enabling determination of the optimal contribution of the Reynolds force vector. The effects of divergence schemes and turbulence models are investigated by examining flow over a 30P30N airfoil. The C2ADA model, employing two distinct schemes, demonstrates significant improvements in velocity estimation, but the first-order scheme introduces excessive dissipation, resulting in an under-prediction of spanwise vorticity. The C2ADA model combined with different eddy-viscosity models uniquely recovers the Reynolds force vectors and obtains mean fields that outperform those achieved solely through conventional eddy viscosity models. The practicability of the C2ADA model for capturing complex flow phenomena is confirmed by applying it to study three-dimensional flow over a 65° delta wing. Despite limited wall pressure observations, the C2ADA model has shown a notable improvement in accurately estimating the intensity and location of both the primary and secondary vortices. Recovery errors in the apex region are significantly diminished by incorporating a paucity of observations account for the effect of inboard vortex. The study broadens the applicability of continuous adjoint-based approaches for modeling compressible flow, as our C2ADA approach is easily implemented in existing computational fluid dynamics solvers and has significantly higher computational efficiency than other approaches.

Adaptive Rank-One Matrix Completion Using Sum of Outer Products

Matrix completion refers to recovering a matrix from a small subset of its entries. It is an important topic because numerous real-world data can be modeled as low-rank matrices. One popular approach for matrix completion is based on low-rank matrix factorization, but it requires knowing the matrix rank, which is difficult to accurately determine in many practical scenarios. We propose a novel algorithm based on rank-one approximation that a matrix can be decomposed as a sum of outer products. The key idea is to find the basis vectors of the underlying matrix according to the observed entries, and gradually increase the vector number until an appropriate rank estimate is reached. In contrast to the conventional rank-one schemes that employ unchanging rank-one basis matrices, our algorithm performs completion from the vector viewpoint and is able to generate continuously updated rank-one basis matrices. Besides, we theoretically show that the developed method has a linear convergence rate and a smaller recovery error than existing rank-one based algorithms. Experimental results using both synthetic data and real-world images demonstrate that our solution has the best recovery performance among the competing algorithms when the observations are contaminated by Gaussian noise.

Improved analytical learning proximal operator method for sparse recovery

Sparse recovery (SR) has received considerable attention in the last decades. Various iterative algorithms have been proposed to solve the SR problem, but most of them are unsatisfactory in terms of convergence accuracy and speed. Recently, deep unfolding methods achieve a dramatic improvement in convergence by unfolding iterative SR algorithms into deep neural networks and learning their parameters from training data, e.g., Learning Proximal Operator Method (LePOM) and Analytical LePOM (ALePOM). However, according to theoretical analysis, the recovery error bound of ALePOM should be further reduced as its network parameters are learned to fit all training data. To solve this problem, we design a new network named Improved ALePOM (I-ALePOM) by introducing a Long Short-Term Memory (LSTM) cell into each layer of ALePOM, which helps to adaptively compute the thresholds and step-sizes for each training data. By doing so, the recovery error bound becomes tighter and higher sparse recovery performance can be achieved. Furthermore, we extend the proposed I-ALePOM from the 1-D vector form to the 2-D matrix form, reducing the memory and computation burdens for practical sparse matrix recovery applications. Various numerical simulations verify that the proposed method has a higher performance than the state-of-the-art methods in the literature.

Phantom and clinical evaluation of Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm in 68Ga-PSMA PET-CT studies.

In this study, we aimed to examine the effect of varying β-values in the block sequential regularized expectation maximization (BSREM) algorithm under differing lesion sizes to determine an optimal penalty factor for clinical application. The National Electrical Manufacturers Association phantom and 15 prostate cancer patients were injected with 68Ga-PSMA and scanned using a GE Discovery IQ PET/CT scanner. Images were reconstructed using ordered subset expectation maximization (OSEM) and BSREM with different β-values. Then, the background variability (BV), contrast recovery, signal-to-noise ratio, and lung residual error were measured from the phantom data, and the signal-to-background ratio (SBR) and contrast from the clinical data. The increment of BV using a β-value of 100 was 120.0%, and the decrement of BV using a β-value of 1000 was 40.5% compared to OSEM. As β decreased from 1000 to 100, the [Formula: see text] increased by 59.0% for a sphere with a diameter of 10mm and 26.4% for a sphere with a diameter of 37mm. Conversely, [Formula: see text] increased by 140.5% and 29.0% in the smallest and largest spheres, respectively. Furthermore, the Δ[Formula: see text] and Δ[Formula: see text] were - 41.1% and - 36.7%, respectively. In the clinical study, OSEM exhibited the lowest SBR and contrast. When the β-value was reduced from 500 to 100, the SBR and contrast increased by 69.7% and 71.8% in small and 35.6% and 33.0%, respectively, in large lesions. Moreover, the optimal β-value decreased as lesion size decreased. In conclusion, a β-value of 400 is optimal for small lesion reconstruction, while β-values of 600 and 500 are optimal for large lesions in phantom and clinical studies, respectively.

Monocular polarized three-dimensional absolute depth reconstruction technology for multi-target scenes.

The traditional polarization three-dimensional (3D) imaging technology has limited applications in the field of vision because it can only obtain the relative depth information of the target. Based on the principle of polarization stereo vision, this study combines camera calibration with a monocular ranging model to achieve high-precision recovery of the target's absolute depth information in multi-target scenes. Meanwhile, an adaptive camera intrinsic matrix prediction method is proposed to overcome changes in the camera intrinsic matrix caused by focusing on fuzzy targets outside the depth of field in multi-target scenes, thereby realizing monocular polarized 3D absolute depth reconstruction under dynamic focusing of targets at different depths. Experimental results indicate that the recovery error of monocular polarized 3D absolute depth information for the clear target is less than 10%, and the detail error is only 0.19mm. Also, the precision of absolute depth reconstruction remains above 90% after dynamic focusing on the blurred target. The proposed monocular polarized 3D absolute depth reconstruction technology for multi-target scenes can broaden application scenarios of the polarization 3D imaging technology in the field of vision.

Multi-Angle Models and Lightweight Unbiased Decoding-Based Algorithm for Human Pose Estimation

When a top-down method is taken to the task of human pose estimation, the accuracy of joint point localization is often limited by the accuracy of human detection. In addition, conventional algorithms commonly encode the image to generate a heat map before processing, but the systematic error in decoding the heat map back to the original image has an impact on the positioning. Therefore, to address the two problems, we propose an algorithm that uses multiple angle models to generate the human boxes and then performs lightweight decoding to recover the image. The new boxes can better fit humans and the recovery error can be reduced. First, we split the backbone network into three sub-networks, the first sub-network is responsible for generating the original human box, the second sub-network is responsible for generating a coarse pose estimation in the boxes, and the third sub-network is responsible for a high-precision pose estimation. In order to make the human box fit the human body better, with only a small number of interfering pixels inside the box, models of the human boxes with multiple rotation angles are generated. The results from the second sub-network are used to select the best human box. Using this human box as input to the third sub-network can significantly improve the accuracy of the pose estimation. Then to reduce the errors arising from image decoding, we propose a lightweight unbiased decoding strategy that differs from traditional methods by combining multiple possible offsets to select the direction and size of the final offset. On the MPII dataset and the COCO dataset, we compare the proposed algorithm with 11 state-of-the-art algorithms. The experimental results show that the algorithm achieves a large improvement in accuracy for a wide range of image sizes and different metrics.

Super resolution direction finding technique of vortex electromagnetic wave radar in missing mode

Vortex electromagnetic waves have superior performance over electromagnetic waves. In order to improve the radar super-resolution lateralization technique in its missing modes, this study proposes to start from the perspective of mode missing. The missing modes are reconstructed using the Adaptive Step Size Gradient Descent (ASSGD) method by exploiting the features of the missing modes. The linear minimum mean square error (LMMSE) estimation method is also used to solve the problem of poor reconstruction accuracy due to the Missing Modes. The Missing Modes Iterative Adaptive Approach (MMIAA) algorithm and Missing Modes Sparse Learning via Iterative Minimization (MMSLIM) algorithm are then used. Minimization (MMSLIM) algorithm to recover missing modes. The results showed that the RMSEs of the recovery errors of MMSLIM, MMIAA and ASSGD were 0.16, 0.31 and 0.82 respectively at a modal missing ratio of 0.7, while ASSGD fails to recover the missing modal data at a modal missing ratio of 0.9. The overall data quality of the azimuthally estimated RMSE was average when the signal-to-noise ratio was at [–5, 10] dB. And the curve becomes flatter when it reaches 15 dB or more, indicating that MMSLIM, MMIAA has important theoretical and practical value.

Green spectrophotometric method for the spectral pattern recognition based on fuzzy inference system compared to continuous wavelet transform for the quantitative determination of anti-psoriasis drugs in commercial skin ointment formulation: Comparison with HPLC

In this study, two chemometrics methods, including fuzzy inference system (FIS) along with principal component analysis (PCA) and continuous wavelet transform (CWT) were proposed for the simultaneous spectrophotometric estimation of betamethasone dipropionate (BMD) and calcipotriol (CP) in pharmaceutical dosage form without any pretreatment. The mean recovery and root mean square error (RMSE) related to the nine rules of the FIS was 100.14%, 100.72% and 0.016 and 0.020 for BMD and CP, respectively. CWT with wavelet families of Daubechies (db2) and Coiflet (Coif2) were selected to give the best zero crossing point at a wavelength of 254 nm and 265 nm, as well as the highest coefficient of determination (R2) for BMD and CP, respectively. The limit of detection (LOD) and limit of quantitation (LOQ) were found to be 5 × 10−5, 1.7 × 10−3 μg/mL and 0.0277, 0.0225 μg/mL through the calibration graphs in the linear concentration ranges of 1–10 μg/L for BMD and CP, respectively. The selected wavelet families were successfully applied for chemometric analysis of BMD and CP in synthetic mixtures with satisfactory mean recovery values of 94.67% and 102.43% for BMD and CP, respectively. The proposed spectrophotometric methods were statistically compared to high-performance liquid chromatography (HPLC) using analysis of variance (ANOVA). The proposed analytical techniques were found to be easy, fast, precise, and robust with less solvent consumption for the concurrent analysis of BMD and CP, which can be used instead of chromatography methods in pharmaceutical quality control laboratories.

Single and multiple illuminant estimation using convex functions

The lighting situation in which a picture was taken has an impact on its color. Illuminant estimation is crucial in computer vision because the colors of objects vary as illumination changes. For this reason, numerous methods for estimating the illuminant have been suggested. In this paper, we suggest a novel statistic-based method for estimating single and multiple illuminants using convex functions. In this respect, convex functions are used in the two subsequent steps of normalization and weight creation. After using weighted K-means to segment the picture, each segment’s associated illuminations are determined. The illumination map for the input image is estimated as a final stage. In this study, we also analyze the effect of convexity on color constancy algorithms and present proofs for the convexity of some statistic-based algorithms. Four different single and multi-illuminant datasets have been used to evaluate the proposed algorithm in terms of two evaluation metrics; recovery and reproduction angular error. We believe that the proposed method could be considered one of the statistical state-of-the-art algorithms. In addition, it has competitive results when compared to most learning-based and deep-learning methods. Further advantages of the proposed algorithm include its simplicity of implementation and low execution time.

On the use of cross-validation for the calibration of the adaptive lasso.

Cross-validation is the standard method for hyperparameter tuning, or calibration, of machine learning algorithms. The adaptive lasso is a popular class of penalized approaches based on weighted L1 -norm penalties, with weights derived from an initial estimate of the model parameter. Although it violates the paramount principle of cross-validation, according to which no information from the hold-out test set should be used when constructing the model on the training set, a "naive" cross-validation scheme is often implemented for the calibration of the adaptive lasso. The unsuitability of this naive cross-validation scheme in this context has not been well documented in the literature. In this work, we recall why the naive scheme is theoretically unsuitable and how proper cross-validation should be implemented in this particular context. Using both synthetic and real-world examples and considering several versions of the adaptive lasso, we illustrate the flaws of the naive scheme in practice. In particular, we show that it can lead to the selection of adaptive lasso estimates that perform substantially worse than those selected via a proper scheme in terms of both support recovery and prediction error. In other words, our results show that the theoretical unsuitability of the naive scheme translates into suboptimality in practice, and call for abandoningit.