Separation Matrix Research Articles

Exploring Bounded Component Analysis Using an ℓ∞ Norm Criterion

In this paper we propose a new criterion for the Blind Source Separation (BSS) of antisparse bounded sources, based on the sum of the ℓ∞-norm of the sources. Based on the observation that the mixing process of bounded sources with any mixing matrix with unitary Frobenius norm will increase the ℓ∞-norm of the sources, unless it is the identity matrix, the minimization of the sum of the ℓ∞-norm of the sources can be used for the estimation of a separation matrix. To that, a Principle Component Analysis technique followed by a Givens Rotations based optimization method can be used for the separation of independent bounded sources. Also, the Givens Rotations based optimization method can be used for the separation of correlated bounded sources mixed by a rotation matrix. We theoretically analyze the proposed criterion and assess its performance through numerical simulations involving three distinct types of bounded signals. Our theoretical and experimental findings underscore the efficacy of the ℓ∞ norm as a suitable contrast function for antisparse bounded sources, showcasing its superior performance relative to a state-of-the-art algorithm.

Oriental covalent immobilization of N-glycan binding protein via N-terminal selective modification

Lectin affinity chromatography is one of powerful tools for the study of protein glycosylation. Different lectin proteins can recognize different structures of monosaccharides or oligosaccharide units, allowing for the selective separation of glycopeptides or glycoproteins containing different polysaccharide structures. However, the N-glycans were only partially captured by most of common lectins, reducing the coverage rate of identifying N-glycoconjugates. Recently, it has been reported that the engineering variant of glycan binding protein Fbs1 has a high affinity for innermost Man3GlcNAc2 structure and is able to bind diverse types of N-glycans, which can be suitable for the analysis of protein N-glycosylation. However, efficient immobilization of protein to separation matrix is particularly challenging as it requires the functionality and integrity of the protein to be preserved. Herein, we describe a simple and robust strategy for oriental covalent immobilization of proteins on magnetic nanoparticles by N-terminal selective labeling techniques. We inserted the enterokinase cleavage site to produce the specific N- terminal glycine of protein. Under physiological conditions, the protein was immobilized on the surface of the MNPs by this glycine tag, and the enrichment process could be completed within 30 min. A whole enrichment and purification of glycan and glycopeptides were completed and analyzed by MALDI TOF-MS. The functional materials achieved stable enrichment of glycan structure in different enzyme digestion systems or complex samples, showing excellent anti-interference and applicability.

Phonon Engineering in Solid Polymer Electrolyte toward High Safety for Solid-State Lithium Batteries.

Extensively-used rechargeable lithium-ion batteries (LIBs) face challenges in achieving high safety and long cycle life. To address such challenges, ultrathin solid polymer electrolyte (SPE) is fabricated with reduced phonon scattering by depositing the composites of ionic-liquid (1-ethyl-3-methylimidazolium dicyamide, EMIM:DCA), polyurethane (PU) and lithium salt on the polyethylene separator. The robust and flexible separator matrix not only reduces the electrolyte thickness and improves the mobility of Li+, but more importantly provides a relatively regular thermal diffusion channel for SPE and reduces the external phonon scattering. Moreover, the introduction of EMIM:DCA successfully breaks the random intermolecular attraction of the PU polymer chain and significantly decreases phonon scattering to enhance the internal thermal conductivity of the polymer. Thus, the thermal conductivity of the as-obtained SPE increases by approximately six times, and the thermal runaway (TR) of the battery is effectively inhibited. This work demonstrates that optimizing thermal safety of the battery by phonon engineering sheds a new light on the design principle for high-safety Li-ion batteries.

Blind source separation algorithm for noisy hydroacoustic signals based on decoupled convolutional neural networks

Wireless communication technology has been widely used in marine engineering, marine ranching and Marine environmental monitoring. However, structural redundancy and functional confusion exist in applying neural networks in signal separation technology in underwater communication environments, which can result in a slower rate of signal separation and lead to confusion of parameters during transfer learning. Based on this, an end-to-end, internal functionally structured decoupled neural network (D-CNN) blind source separation (BSS) model is proposed in this paper, which can realize a neural network BSS algorithm with a well-defined structure and function. The one-dimensional convolutional neural network layer is used in algorithm to automatically extract observed signal's features, based on the features, and there are two generation modules of separation matrix and scaling coefficients. Then the two modules can be used to separate the observed signal and adjust the signal coefficients to obtain the separated signal. Finally the transfer learning technique is used to generalize the model, which reduces the transfer cost of the model in different application scenarios. Experimental results show that when the communication distance is set to 0.02 km–2 km, the MSE of independent signal and related signal can be reduced by 14.24% and 14.95% respectively compared with the nearest Neural FCA algorithm. The results prove that the proposed algorithm can accurately estimate the source signal and improve the signal reception quality.

Polyvinyl Alcohol/Zr-based Metal Organic Framework Mixed-matrix Membranes Synthesis and Application for Hydrogen Separation

Membrane gas separation is an environmentally friendly and economical method used to separate valuable gases, industrial process gas wastes, and carbon dioxide from mixed gases. The most important part of this method is the membranes. Gas separation membranes are expected to have high separation and permeability performance, high mechanical strength, easy and fast production capability, and low prices. Polymer-based membranes are mostly preferred depending on the ease of modification capability. In this study, a zirconium-based metal organic framework (Zr-MOF, MIL-140 A) was synthesized and used as a filler within polyvinyl alcohol (PVA) matrix for the selective separation of hydrogen (H2) from carbon dioxide (CO2). The effect of MIL-140 A addition on the mechanical, structural, and morphological properties of PVA was evaluated. The MIL-140 A significantly improved the mechanical strength of the membrane. According to the gas separation results, the increasing concentration of MIL-140 A increased the selective separation performance of the nanocomposite membrane. The highest mechanical strength (43.1 MPa) and best film-forming ability were obtained with 3 wt% MIL-140 A loaded membrane. The ideal H2/CO2 selectivity and hydrogen permeability were obtained as 5.6 and 944 Barrer, respectively at 2 bar feed pressure and room temperature. The highest ideal H2/CO2 selectivity was obtained as 6.3 with the H2 permeability of 959 Barrer when the MIL-140 A ratio was 4 wt%.

An initial study of cell separation based on mechanical properties using a sponge-like monolithic polymer

We developed a novel cell separation method based on the mechanical properties using a sponge-like monolithic polymer (SPM) in a spin-column format capable of high throughput and mass processing for cell diagnosis and separation. The continuous large flow pores of the monolithic skeleton around 200 μm were expected to act as a sieving matrix for large flexible molecules such as cells larger than 10 μm in diameter, based not only on the size but also on the mechanical deformability of the cells. The passage rates of rigid polystyrene beads ranging in size from 1 to 10 μm were investigated and demonstrated that the spin column acted as a separation matrix rather than a size-based cut-off filter in the 60 and 200 μm pores of the SPM. Two cell types, adherent cells (HeLa cells) and suspension cells (THP-1 cells), showed different passage behavior in the spin column, and 70 % and 30 % of the cells, respectively, were trapped in the column and never eluted. To investigate how the mechanical deformability of the cells affects the passage behavior, glutaraldehyde treatment, which denatured the proteins and changed the elastic moduli, was performed and compared. As a result, the fixed cells drastically reduced the passage rate and became trapped inside the SPM column. These initial studies explored a new application field of SPM for high throughput cell separation based on the mechanical properties of the cells despite the same size, and contribute to a new cell assay method prior to cell transplantation.

Separable sample covariance matrices under elliptical populations with applications

This paper is to investigate the spectral properties of separable covariance matrices under elliptical populations. The separable covariance matrix model can handle both cross-row and cross-column correlations thus gain more popularity recently. Under the high-dimensional setting where the dimension p p and the sample size n n tend to infinity proportionally, we find the limit of the empirical spectral distribution and establish the central limit theorems (CLT) for linear spectral statistics of such kinds of sample covariance matrices. Some applications of our established CLT are also given.

Exploring the potential of using ion mobility-mass spectrometry to separate matrix interferences from analytes in food control

During residue analysis in complex matrices for food safety purposes, interfering signals can sometimes overlap with those of the analyte of interest. Access to an additional separation dimension besides chromatographic and mass separation, such as ion mobility, can aid in removing interfering signals, allowing for correct analyte identification in these cases. In our laboratory, during routine LC−MS/MS analysis of liver samples for growth promoter residues, an interfering signal was found that matches the retention time and m/z values for stanozolol, a synthetic anabolic steroid. In the present work, the performance of a liquid chromatography coupled to ion mobility mass spectrometry (LC−IM−MS) method has been evaluated to study whether this LC−MS/MS false positive in liver samples could be eliminated by LC−IM−MS analysis. A cyclic ion mobility system already allowed the separation of stanozolol from the interfering peak after only one pass, showing a significant improvement compared to the conventional LC−MS/MS method. Additionally, collisional cross section (CCS) values were calculated and successfully compared with those from literature for identification purposes, eventually allowing both the identification and quantification of stanozolol in this complex matrix.

Co‐channel interference cancellation based on BSS for multi‐satellite MIMO communication systems with FFR

SummaryMultiple‐input multiple‐output (MIMO) technology can boost the capacity of conventional satellite communications, and full frequency reuse (FFR) can further improve the capacity by making full use of frequency resources. However, FFR will cause severe co‐channel interference and lead to a degradation of channel capacity. Blind source separation (BSS) algorithm can be used to eliminate co‐channel interference and does not require prior information, which avoids the extra occupation of channel frequency resources. While the multi‐satellite MIMO communication system have delay mixing characteristic, the separation performance will be degraded if the delay is ignored. In this paper, DMBSS algorithm for delay mixing is proposed. Firstly, the DMBSS algorithm utilizes Taylor expansion to achieve dimension expansion of observed signals and so as to transform the delay mixing into instantaneous mixing with extended dimension. Secondly, complex non‐orthogonal joint diagonalization algorithm is used to solve the separation matrix equation of extended observation signals and extended source signals and thus to eliminate the co‐channel interference. Simulation results show that for multi‐satellite MIMO communication scenarios with FFR, the proposed algorithm can effectively improve the performance of co‐channel interference cancellation and reduce the value of BER. Furthermore, the influence of order of Taylor expansion on the separation performance is also analyzed and simulated in this paper to show the cost performance of the proposed algorithm.

Zhang neural networks: an introduction to predictive computations for discretized time-varying matrix problems

This paper wants to increase our understanding and computational know-how for time-varying matrix problems and Zhang Neural Networks. These neural networks were invented for time or single parameter-varying matrix problems around 2001 in China and almost all of their advances have been made in and most still come from its birthplace. Zhang Neural Network methods have become a backbone for solving discretized sensor driven time-varying matrix problems in real-time, in theory and in on-chip applications for robots, in control theory and other engineering applications in China. They have become the method of choice for many time-varying matrix problems that benefit from or require efficient, accurate and predictive real-time computations. A typical discretized Zhang Neural Network algorithm needs seven distinct steps in its initial set-up. The construction of discretized Zhang Neural Network algorithms starts from a model equation with its associated error equation and the stipulation that the error function decrease exponentially fast. The error function differential equation is then mated with a convergent look-ahead finite difference formula to create a distinctly new multi-step style solver that predicts the future state of the system reliably from current and earlier state and solution data. Matlab codes of discretized Zhang Neural Network algorithms for time varying matrix problems typically consist of one linear equations solve and one recursion of already available data per time step. This makes discretized Zhang Neural network based algorithms highly competitive with ordinary differential equation initial value analytic continuation methods for function given data that are designed to work adaptively. Discretized Zhang Neural Network methods have different characteristics and applicabilities than multi-step ordinary differential equations (ODEs) initial value solvers. These new time-varying matrix methods can solve matrix-given problems from sensor data with constant sampling gaps or from functional equations. To illustrate the adaptability of discretized Zhang Neural Networks and further the understanding of this method, this paper details the seven step set-up process for Zhang Neural Networks and twelve separate time-varying matrix models. It supplies new codes for seven of these. Open problems are mentioned as well as detailed references to recent work on discretized Zhang Neural Networks and time-varying matrix computations. Comparisons are given to standard non-predictive multi-step methods that use initial value problems ODE solvers and analytic continuation methods.

RGMU: A High-flexibility and Low-cost Reconfigurable Galois Field Multiplication Unit Design Approach for CGRCA

Finite field multiplication is a non-linear transformation operator that appears in the majority of symmetric cryptographic algorithms. Numerous specified finite field multiplication units have been proposed as a fundamental module in the coarse-grained reconfigurable cipher logic array to support more cryptographic algorithms; however, it will introduce low flexibility and high overhead, resulting in reduced performance of the coarse-grained reconfigurable cipher logic array. In this article, a high-flexibility and low-cost reconfigurable Galois field multiplication unit (RGMU) is proposed to balance the tradeoffs between the function, delay, and area. All the finite field multiplication operations, including maximum distance separable matrix multiplication, parallel update of Fibonacci linear feedback shift register, parallel update of Galois linear feedback shift register, and composite field multiplication, are analyzed and two basic operation components are abstracted. Further, a reconfigurable finite field multiplication computational model is established to demonstrate the efficacy of reconfigurable units and guide the design of RGMU with high performance. Finally, the overall architecture of RGMU and two multiplication circuits are introduced. Experimental results show that the RGMU can not only reduce the hardware overhead and power consumption but also has the unique advantage of satisfying all the finite field multiplication operations in symmetric cryptography algorithms.

Efficient performance of natural polysaccharide-based mixed matrix membrane for CO2/H2 and CO2/N2 mixed gas separation

In this work, fabricating a guar gum (GG) based membrane was modified with chitosan (CH) to separate CO2 from H2 and N2. The cross-linking of GG and CH enhances the tensile strength (TS) by 20.89 % and elongation of break (EB) by 58.60 % compared to GG. Furthermore, investigates the effect of graphene oxide (GO) and amine functional GO (AGO) with the GG/CH composite polymer matrix for gas separation. The GG/CH/GO mixed matrix membrane (MMMs) improves 76.66 % of CO2/H2 and 89.40 % of CO2/N2, and GG/CH/AGO enhances 77.07 % of CO2/H2 and 89.69 % of CO2/N2 compared to GG/CH membrane. Additionally, TS and EB improved by 10.25 % and 66.23 % in GG/CH/GO and 38.56 % and 72.52 % in GG/CH/AGO compared to GG/CH. Additionally, Pd-nanoparticles are dispersed into the membrane to enhance the H2 gas separation performance; the separation performance of CO2/H2 in GG/CG/GO/Pd and GG/CH/AGO/Pd was 37.17 and 44.34, respectively. The FTIR and XRD characterize the membrane to identify the functional group and nature of the membrane. The SEM-EDS show good compatibility between polymer and filler. Henceforth, the study suggests the GG/CH composite polymer modified with filler (GO or AGO) and Pd nanoparticles MMMs is favourable for achieving high selectivity for CO2 gas over other gasses.

Next-Generation Battery Components Derived from Soft Dendritic Colloids

Growing demands for energy storage are causing rapid development of next-generation batteries. Researchers typically use commercially available Li-ion components and processing techniques to investigate new chemistries and cell architectures. Herein, we propose an emerging materials-development platform: shear- driven precipitation of polymeric materials to develop application-specific battery components. In this process, polymer particles with high aspect ratios, called soft dendritic colloids (SDCs), are formed via turbulent solvent-nonsolvent induced phase separation. Two specific component applications are presented.The first application is nano-composite redox active polymer electrodes for Li-ion batteries. Redox polymer electrodes are currently limited by low intrinsic electronic conductivity. As a result, large amounts of conductive additives are added to the composite electrodes, lowering the gravimetric performance. The network forming capabilities of the SDC morphology provides intimate contact between active material and conductive material to enable free-standing electrodes that are produced via simple vacuum filtration. We present cyclic voltammetry and charge/discharge data using poly(2,2,6,6-tetramethyl-1-piperidinyloxy methacrylate) as the SDC material.The second application is composite separator assemblies to mitigate lithium polysulfide shuttling in Li-S batteries. Polysulfide shuttling is considered a technological hurdle plaguing Li-S battery development. Separators can be functionalized to reduce polysulfide shuttling by incorporating additives onto or within the separator matrix, which either bind or repel the soluble species to prevent crossover to the anode. We demonstrate that SDC materials are a promising platform for fabricating nanocomposite separators using a variety of nanomaterials. The nano composite separators show increased thermal stability, cycling stability, and polysulfide rejection as compared to commercial materials.

A priori free spectral unmixing with periodic absorbance changes: application for auto-calibrated intraoperative functional brain mapping.

Spectral unmixing designates techniques that allow to decompose measured spectra into linear or non-linear combination of spectra of all targets (endmembers). This technique was initially developed for satellite applications, but it is now also widely used in biomedical applications. However, several drawbacks limit the use of these techniques with standard optical devices like RGB cameras. The devices need to be calibrated and a a priori on the observed scene is often necessary. We propose a new method for estimating endmembers and their proportion automatically and without calibration of the acquisition device based on near separable non-negative matrix factorization. This method estimates the endmembers on spectra of absorbance changes presenting periodic events. This is very common in in vivo biomedical and medical optical imaging where hemodynamics dominate the absorbance fluctuations. We applied the method for identifying functional brain areas during neurosurgery using four different RGB cameras (an industrial camera, a smartphone and two surgical microscopes). Results obtained with the auto-calibration method were consistent with the intraoperative gold standards. Endmembers estimated with the auto-calibration method were similar to the calibrated endmembers used in the modified Beer-Lambert law. The similarity was particularly strong when both cardiac and respiratory periodic events were considered. This work can allow a widespread use of spectral imaging in the industrial or medical field.

WAS: improved white-box cryptographic algorithm over AS iteration

The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution environment. In order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes, WAS, an improved white-box cryptographic algorithm over AS iteration is proposed. This scheme utilizes the AS iterative structure to construct a lookup table with a five-layer ASASA structure, and the maximum distance separable matrix is used as a linear layer to achieve complete diffusion in a small number of rounds. Attackers can be prevented from recovering the key under black-box model. The length of nonlinear layer S and affine layer A in lookup table is 16 bits, which effectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the white-box model, while WAS possesses weak white-box (32 KB, 112)-space hardness to satisfy anti-code lifting security. WAS has provable security and better storage cost than existing schemes, with the same anti-key extraction security and anti-code lifting security, only 128 KB of memory space is required in WAS, which is only 14% of SPACE-16 algorithm and 33% of Yoroi-16 algorithm.

A Separation Method for Electromagnetic Radiation Sources of the Same Frequency

To separate electromagnetic interference sources with an unknown source number, a new separation method is proposed, which includes five key steps: spatial spectrum estimation, source number and direction-of-arrival estimation, mixed matrix estimation, separation matrix estimation, and source signal recovery. A pseudospatial spectrum estimation network based on a convolutional neural network is proposed to estimate the number of electromagnetic radiation sources, their direction of arrival, and the mixing matrix. A new loss function is designed as an optimization criterion for estimating the separation matrix. To ensure generalization, both simulated and measured datasets are used to train the proposed network. Experimental results demonstrate that the proposed separation method outperforms existing source separation techniques in terms of correlation coefficient, root mean square error, and running time. Importantly, it exhibits strong performance in underdetermined cases, as well as in overdetermined or determined cases.

Mixed-matrix membranes comprising porous organic molecular cage for efficient CO2 capture

Membrane-based separation technology exhibits significant potential in the fields of CO2 capture and gas purification. Mixed-matrix membranes (MMMs) integrate the easy processing of polymeric materials with excellent transport properties of fillers, and thereby have become a focus for the next-generation gas separation membranes. Herein, we demonstrated a novel mixed-matrix membrane comprising porous organic molecular cages (POCs) and amine-rich polyvinylamine (PVAm) polymer matrix for efficient CO2 separation. Micro-sized CC3 crystals featuring a pore size of ∼4.9 Å, a high micropore volume of 0.16 cm3 g−1 and specific surface area of 326 m2 g−1 were synthesized and immobilized onto the surface of the PVAm thin selective layer to generate rapid CO2-transport channels. The resulting CC3/PVAm/mPSf MMM displayed excellent binary gas mixture (CO2/N2 15/85 vol %) separation performance, with a high CO2 permeance of 1546 GPU, and appreciable CO2/N2 selectivity of 33 at 1.5 bar, which was superior to most reported POCs-based membranes and thin film composite membranes, accompanied with excellent long-term operational stability. The CO2-selective separation facilitated by the incorporation of POCs provided a new inspiration for the development of novel MMMs for efficient CO2 capture.

Beyond islands: a free probabilistic approach

We give a free probabilistic proposal to compute the fine-grained radiation entropy for an arbitrary bulk radiation state, in the context of the Penington-Shenker-Stanford-Yang (PSSY) model where the gravitational path integral can be implemented with full control. We observe that the replica trick gravitational path integral is combinatorially matching the free multiplicative convolution between the spectra of the gravitational sector and the matter sector respectively. The convolution formula computes the radiation entropy accurately even in cases when the island formula fails to apply. It also helps to justify this gravitational replica trick as a soluble Hausdorff moment problem. We then work out how the free convolution formula can be evaluated using free harmonic analysis, which also gives a new free probabilistic treatment of resolving the separable sample covariance matrix spectrum.The free convolution formula suggests that the quantum information encoded in competing quantum extremal surfaces can be modelled as free random variables in a finite von Neumann algebra. Using the close tie between free probability and random matrix theory, we show that the PSSY model can be described as a random matrix model that is essentially a generalization of Page’s model. It is then manifest that the island formula is only applicable when the convolution factorizes in regimes characterized by the one-shot entropies. We further show that the convolution formula can be reorganized to a generalized entropy formula in terms of the relative entropy.

A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR.

Smoothed separable nonnegative matrix factorization

Given a set of data points belonging to the convex hull of a set of vertices, a key problem in linear algebra, signal processing, data analysis and machine learning is to estimate these vertices in the presence of noise. Many algorithms have been developed under the assumption that there is at least one nearby data point to each vertex; two of the most widely used ones are vertex component analysis (VCA) and the successive projection algorithm (SPA). This assumption is known as the pure-pixel assumption in blind hyperspectral unmixing, and as the separability assumption in nonnegative matrix factorization. More recently, Bhattacharyya and Kannan (ACM-SIAM Symposium on Discrete Algorithms, 2020) proposed an algorithm for learning a latent simplex (ALLS) that relies on the assumption that there is more than one nearby data point to each vertex. In that scenario, ALLS is probabilistically more robust to noise than algorithms based on the separability assumption. In this paper, inspired by ALLS, we propose smoothed VCA (SVCA) and smoothed SPA (SSPA) that generalize VCA and SPA by assuming the presence of several nearby data points to each vertex. We illustrate the effectiveness of SVCA and SSPA over VCA, SPA and ALLS on synthetic data sets, on the unmixing of hyperspectral images, and on feature extraction on facial images data sets. In addition, our study highlights new theoretical results for VCA.