Channel Matrix Research Articles

Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation.

This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications.

Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed.

Electromagnetic fields transformation in UWB infinite antenna arrays in the cluster excitation mode

Infinite ultra-wideband (UWB) arrays of TEM horns and Vivaldi antennas were considered. At the first stage we used an array model in the quasi-periodic excitation mode in the form of a Floquet channel. It was implemented in the HFSS electromagnetic modeling system. At the second stage the array parameters in the cluster excitation mode were determined using the calculated scattering matrix of the Floquet channel. Two clusters of TEM horns and Vivaldi antennas were analyzed. They were finite along one coordinate and infinite along the other. It was investigated how the cluster size, frequency, amplitude distribution of exciting waves, scanning in the sector of angles affect the shape of the amplitude-phase distribution of the field in the array aperture. It was shown that the field distribution in the emitting aperture may differ significantly from the distribution of exciting waves at the inputs of the array elements. An explanation of this effect based on the representation of the field in the array in the form of a superposition of its eigen waves was proposed.

Enhanced fatigue performance of γ/γ′ dual-phase single-crystal superalloys with graded surface nanostructures

Shot peening (SP) is a common surface-strengthening technique for improving the fatigue resistance of γ/γ′ dual-phase Ni-based superalloys, yet the coordinated deformation mechanism of γ/γ′ dual-phase architecture and its role in fatigue strengthening remain elusive. In this study, unique graded surface nanostructures were reported for the first time in shot peened single-crystal (SX) superalloys that possessed a six-fold fatigue life improvement compared to their non-treated counterparts. The graded nanostructures were composed of (i) outermost “fully nanocrystallized zone” (average grain size ∼55 nm) with nearly complete dissolution of γ′ precipitates, (ii) “transition zone” with partially fragmented γ′ precipitates, and (iii) “partially nanocrystallized γ channel zone” with the emergence of nanograins merely in the γ channels. These observations advance the previous understanding that only fully nanocrystallized zone is present in the SP-treated γ/γ′ SX superalloys. The graded surface nanostructures, together with work hardening and compressive residual stress, hindered strain localization and delayed crack initiation in the original soft γ matrix channels, thus prolonging the fatigue life. The collective outcomes of this work not only provide in-depth insights into the deformation mechanisms in shot-peened γ/γ′ dual-phase superalloys but also can be used to further tune their surface nanostructures and hence mechanical properties.

The Inter-channel Interference Cancellation Algorithm for Wireless Magnetic Induction MIMO Communication System Based on Zero-forcing Precoding

Abstract The wireless magnetic induction MIMO system can enhance the data transmission rate in near-field magnetic induction communication, but it also suffers from severe inter-channel interference issues. This paper introduces precoding techniques into the wireless magnetic induction MIMO system, employing the zero-forcing precoding algorithm to eliminate interference between different channels. Firstly, leveraging the characteristic that the channel state of magnetic induction transmit-receive coils is determined solely by the physical parameters of the coils, channel estimation is performed to obtain the channel gain matrix for wireless magnetic induction communication. Subsequently, the obtained channel matrix is subjected to zero-forcing precoding to eliminate interference caused by coupling between different channels. Simulation results indicate that the data transmission rate of the wireless magnetic induction MIMO system is significantly higher after applying zero-forcing precoding compared to the rate without it. Additionally, the system’s data transmission rate decreases as the distance between the transmitting and receiving coils increases.

Cellular NS1-BP protein interacts with the mRNA export receptor NXF1 to mediate nuclear export of influenza virus M mRNAs

Influenza A viruses have eight genomic RNAs that are transcribed in the host cell nucleus. Two of the viral mRNAs undergo alternative splicing. The M1 mRNA encodes the matrix protein 1 (M1) and is also spliced into M2 mRNA, which encodes the proton channel matrix protein 2 (M2). Our previous studies have shown that the cellular NS1-binding protein (NS1-BP) interacts with the viral non-structural protein 1 (NS1) and M1 mRNA to promote M1 to M2 splicing. Another pool of NS1 protein binds the mRNA export receptor NXF1 (nuclear RNA export factor-1), leading to nuclear retention of cellular mRNAs. Here we show a series of biochemical and cell biological findings that suggest a model for nuclear export of M1 and M2 mRNAs despite the mRNA nuclear export inhibition imposed by the viral NS1 protein. NS1-BP competes with NS1 for NXF1 binding, allowing the recruitment of NXF1 to the M mRNAs after splicing. NXF1 then binds GANP (Germinal-center Associated Nuclear Protein), a member of the TRanscription and EXport complex (TREX)-2. Although both NS1 and NS1-BP remain in complex with GANP-NXF1, they dissociate once this complex docks at the nuclear pore complex (NPC), and the M mRNAs are translocated to the cytoplasm. Since this mRNA nuclear export pathway is key for expression of M1 and M2 proteins that function in viral intracellular trafficking and budding, these viral-host interactions are critical for influenza virus replication.

Simulation of the Process of Temperature Field Changing of a Zr–1%Nb Zirconium Alloy Billet During Equal-Channel Angular Pressing

Using mathematical modeling in the QFORM software package, the data on the temperature distribution of a billet made of a zirconium alloy E110 (Zr–1%Nb) in its longitudinal and transverse sections during an equal-channel angular pressing (ECAP) were obtained. The initial data on the geometric parameters of the billet and the die of the ECAP unit are presented, and the conditions for carrying out the process of severe plastic deformation, as well as the chemical composition and physical characteristics of the Zr–1%Nb alloy necessary for carrying out the computational modeling are described. The values of the billet temperature were determined for the calculated moments of the passage of the billet through the junction of the die channels, which corresponded to 25%, 50%, and 75% of its length. It is shown that during the ECAP process at a given pressing speed, in the section corresponding to the junction of the matrix channels, an increase in the billet temperature relative to its initial heating occurred by an amount of about 10 ºC.

Crosslinking assembly of Propenyl-Calix[4]-Crown-6 interpenetrating functional mesoporous carbon Spheres: High selectivity and stability to Remove Cs(I) from high acid solution

The calix[4]-crown-6 ligand shows high selectivity for Cs(I) over other alkali and alkaline earth metal cations, making it a promising candidate for radiocesium separation. However, its high viscosity and poor adhesion limit its practical use and stability. In this study, an alkenyl-functionalized calix[4]-crown-6 (p-C[4]C6) was developed. Using a mesoporous structure and post-assembly techniques, a composite (p-C[4]C6/MMCs) was created via crosslinking within mesoporous matrix channels. The p-C[4]C6/MMCs exhibited high stability and practicality. The p-C[4]C6/MMCs demonstrated high selectivity, good reusability, and significant adsorption capacity (47.76 mg/g) for Cs(I) in 3.0 M HNO3. Kinetic, isotherm, and thermodynamic studies suggest a spontaneous chemical adsorption mechanism. This research provides insights into the design of calix[4]-crown-6 based materials and offers a viable approach for Cs(I) separation from high-level liquid waste (HLW).

THE PERFORMANCE ANALYSIS OF PRECODED SPACE-TIME FREQUENCY MIMO-GFDM OVER RAYLEIGH FADING CHANNELS

The physical layer is implemented in the present communication era with new multicarrier modulation schemes such as Generalized Frequency Division Multiplexing with Multi-Input and Multi-Output (MIMO-GFDM) antenna systems to achieve good spectral efficiency and diversity order. This paper presents precoded Space-Time-Frequency MIMO-GFDM performance analysis to improve the bit error rate performance without increasing transmission power and bandwidth compared to conventional techniques. The proposed system also enhances the diversity order over frequency selective fading channels. In general, we need to perform channel matrix inversion operations at the receiver or channel precoding matrix operations at the transmitter to detect the symbols of MIMO-GFDM systems. This paper's proposed scheme completes the same task without performing channel matrix inversion. Orthogonal transform techniques such as Haar, Harley, Walsh-Hadamard, and Slant transforms are used as precoders at the transmitter for the proposed scheme. The simulation results are validated on the MATLAB working platform. We have compared the bit error rate of the PSTF-MIMO-GFDM system with Space-Time (ST) and Space Frequency (SF) as baseline schemes and different orthogonal transform precoding techniques.

Channel estimation of massive MIMO FSO communication system using deep attention residual U-Net

Channel estimation in massive-MIMO FSO systems is critical for ensuring reliable data transmission. However, conventional estimators offer limited benefits due to the computational difficulty of accurately estimating the channel. This paper presents a novel approach to estimate channels using an attention residual U-Net (ARU-Net) architecture which utilizes the advantages of both attention and residual connection. In the simulation, the channel matrix has been represented as a 2D image. The proposed model significantly outperforms traditional channel estimation methods and other deep learning models in terms of MSE (10−5 at 25 dB SNR), especially in atmospheric turbulence and other noises.

An investigation of the heat and mass transfer effects in vertical channels with immersible fluid flow through a porous matrix

AbstractIn this article, we investigated the heat and mass transfer of immiscible fluid flow in symmetric two‐vertical regions with a porous matrix in region‐II. The properties of heat generation and absorption are discussed. The channel, consisting of two vertical regions, is filled with a porous matrix with viscous fluids. In both cases, we considered the fluids that undergo Newtonian heat generation and absorption. It was assumed that the porous matrix's channel wall was thermally stable and electrically non‐conducting. The analytical method is used to solve governing equations. Variations in Brinkman Number (Br), Biot Number (Bi), Grashof (Gr) number, viscosity ratio (m), heat generation, and thermal conductivity are illustrated graphically. And we examined the increase in Grashof number, which has effects on fluid flows in both regions. Similarly, the change of heat and momentum transmission in porous media is greatly affected by the viscosity, channel width, source, and sink. Also, when the Grashof number increases, buoyancy increases, leading to an increase in the fluid flow for heat generation and absorption.

Two stage beamforming and combining scheme for FDD massive MIMO systems with multi‐antenna users

AbstractThis paper proposes a two‐stage beamforming and combining scheme in frequency division duplex (FDD) massive multiple‐input multiple‐output (MIMO) systems with multi‐antenna users. Specifically, the proposed scheme is a two‐stage scheme, where the pre‐beamforming matrix and pre‐combining matrix are designed using the channel covariance matrix (CCM) in the first stage. The problem of the pre‐beamforming matrix and pre‐combining matrix design are formulated as an 0–1 quadratic integer programming problem. To solve this problem, it is further transformed into an 0–1 mixed linear integer programming problem. In the second stage, the sparse code multiple access and multi‐user precoding and combining are adopted to mitigate the inter‐user interference. Different from the previous transmission scheme using CCM, the proposed scheme consider the multi‐antenna user system, and uses the CCM to design both the pre‐beamforming and pre‐combining matrices to sparsify the effective channel matrix, such that the overhead of pilot and feedback can be reduced. Moreover, the sparse code multiple access can help to better reduce the inter‐user interference. Simulation results validate the good performance of the proposed scheme.

Enhancing microstructural stability and creep properties by Ta addition in Ni-based single crystal superalloys

To investigate influences of Ta content on microstructural stability during thermal exposure at 900 °C (up to 3000 h) and creep properties at 980 °C/163 MPa in Ni-based SX superalloy, alloys with 7Ta, 8Ta and 9Ta (wt.%) were studied utilizing DSC, SEM, TEM, HR-XRD and APT. The results shows that Ta addition enhanced the partitioning behavior of alloying elements. Besides, lattice misfit was controlled by both alloy composition and temperature. Ta addition increased absolute misfit both room temperature and 980 °C. The difference was that the γ/γ′ lattice misfit changed from positive to negative at room temperature. The γ′ coarsening indicated that Ta addition reduced the effective diffusion coefficient and exacerbated W segregation at γ/γ′ interface, which was beneficial to improve the microstructural stability. Merging of several adjacent γ′ precipitates in preferred direction was observed to be delayed with increasing Ta content, which occurred at 500 h for 7Ta alloy, 800 h for 8Ta alloy and 1000 h for 9Ta alloy, respectively. Ta addition affected the diffusion kinetics and greatly prolonged the steady creep duration, and then resulted in longer creep rupture life. It was discussed based on the γ matrix channels, γ′ raft thickness, the perfection degree of γ′ rafts, residual γ′ precipitates, interfacial dislocation networks and solid solution strengthening.

AFDM signal detection based on message passing scheme

Affine frequency division multiplexing (AFDM) is a new technology that uses discrete affine Fourier transform (DAFT) for multicarrier modulation. It aims to address the issue of unreliable communication in high-speed mobile scenarios. In this paper, we introduce two signal detection algorithms for AFDM that are low complexity. The first algorithm is a message passing (MP) detection algorithm that handles joint interference cancellation and detection by utilizing the inherent sparsity of the channel. The second algorithm is a matched filtering-based message passing (MF-MP) algorithm that utilizes the sparsity of the effective channel matrix of matched filtering. Simulation results show that the performance of the two detection algorithms is superior to that of the minimum mean square error (MMSE) algorithm and the maximum ratio combination (MRC) algorithm.

Fighting Biofilm Bearing Microbes, Global Challenges, Initiatives and Current Novel Therapeutics/Antibiofilm Strategies: A Narrative Scientific Record

Microbial survival against diverse anti-biocidal agents has advance beyond single organism interaction into intricate sessile cells consisting dynamic three-dimensional extracellular complex matrix of water channels. Such complex architecture is built with heterogeneous and variable components that encourage failure of antimicrobial-chemotherapeutic strides especially among biofilm bearing/expressing microbes (BBM). This is a noteworthy public health concern as such occurrence has welcomed global attention in recent times with focus on fighting BBM as well as exploration of novel therapeutic strategies. A narrative scientific description of biofilm as a community of microbes wrapped in extracellular polymeric substances (EPS) was reviewed. The challenges on disease/infection recalcitrance, risk to public health and the necessity to develop novel therapeutics for the management/control and suppression of biofilm linked infections were emphasized. It also summarized potential approaches including the use of antibiofilm/anti-adhesion agents, antimicrobial proteins (AMPs), aptamers, Nano-Particulates (NPs), quorum sensing inhibitors (QSIs), and peptide or protein-based nucleic acids (PNAs) while discussing the agents efficient potential ability. Such anti-biofilm agents/mechanisms and their target-specific-therapeutic relevance promises future for the management/control of biofilm associated microbial infections. Its application may also herald a generational effective combination therapy against illnesses/infections caused by BBM and served as a potential source for BBM global control.

Fault diagnosis of rolling bearings under varying speeds based on gray level co-occurrence matrix and DCCNN

Rolling bearings are widely used in various industries, including rail transit, aerospace, and wind power generation, playing a critical role. However, bearing failures can lead to serious consequences, impacting equipment operation and even causing safety accidents. Hence, the diagnosis of bearing faults is of utmost importance. However, the variable speed conditions experienced during bearing operation pose significant challenges to fault diagnosis. To overcome the limitations of traditional methods in diagnosing bearing faults under variable speed conditions, this paper proposes a fault diagnosis method based on the gray-level co-occurrence matrix (GLCM) and Dual Channel Convolutional Neural Network (DCCNN). The method introduces a two-dimensional grayscale matrix construction (2D-GMC) technique to extract grayscale texture features for fault diagnosis. Additionally, an unconventional kernel design method, based on grayscale image contrast, is proposed to reduce the complexity associated with traditional square kernels. A new DCCNN architecture is developed accordingly. Furthermore, the transfer learning concept is utilized to train the proposed DCCNN model using fault signals at specific rotational speeds. The method intercepts the variable speed into multi-speed short-time series, then constructs gray image under different speed to realize the rapid fault diagnosis of bearings under variable speed conditions.

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.

Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA. The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.

SEAttention-residual based channel estimation for mmWave massive MIMO systems in IoV scenarios

To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems in Internet of Vehicles (IoV) scenarios, the paper proposes a deep learning (DL) algorithm, Squeeze-and-Excitation Attention Residual Network (SEARNet), which integrates Squeeze-and-Excitation Attention (SEAttention) mechanism and residual module. Specifically, SEARNet considers the channel information as an image matrix, and embeds a SEAttention module in residual module to construct the SEAttention-Residual block. Through a data-driven approach, SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism, thereby reducing noise interference and estimating the channel in an accurate and efficient manner. The simulation results show that compared to two traditional and two DL channel estimation algorithms, the proposed SEARNet can achieve a maximum reduction in normalized mean square error (NMSE) of 97.66% and 84.49% at SNR of -10 dB, 78.18% at SNR of 5 dB, and 43.51% at SNR of 10 dB, respectively.

Effect of temperature on the tensile deformation mechanisms of a fourth nickel-based single crystal superalloys

This work conducted tensile experiments from room temperature to 1100 °C on a new fourth-generation single crystal superalloys along [001]. The effect of temperature on the tensile properties and deformation mechanism of the alloy were investigated. Results showed that the yield strength of the alloy reached its peak value (1017 MPa) at 850 °C. From room temperature to 760 °C, the occurrence of stacking faults (SFs) was observed in the γ matrix channel. From the room temperature to 850 °C, the occurrence of stacking faults was observed in the γʹ phase. The interface dislocation networks appeared at 980 °C and above. At room temperature, stacking faults sheared γʹ phase, in which three types of stacking faults occurred, which controlled deformation process. The main deformation mechanism from 650 °C to 850 °C was the shearing of γʹ phase by stacking faults and super dislocations, and at 850 °C, stacking faults in different directions of propagation corresponded to the formation of Lomer-Cottrell locks (L-C locks), which greatly increased the yield strength. At 980 °C and 1100 °C, dislocation climbing and bypassing and anti-phase boundary (APB) coupled dislocation pair shear γʹ phase became the main deformation mechanism.

Hybrid Optimized LMMSE-Based Channel Estimation with Low Power Trellis Coded Modulation

Data transfer in a wireless channel environment is plagued by numerous security issues and power transmission loss. Channel state information is crucial to attaining the performance benefit of reconfigurable intelligent surfaces (RISs) in wireless systems. Because of the Doppler effect, mobility makes accurate channel estimate (CE) more difficult. The parameter estimation approach is employed as an alternative to direct estimation in the channel matrix to address the aforementioned problems. Also, to lower the Mean Square Error (MSE) between the estimated and original channel, training-based CE, such as Linear Minimum Mean Square Error (LMMSE), and Least Square Error (LSE), are widely used in some wireless standards. To optimize the channel, certain intelligent optimized strategies are presented. For the Optimal CE, a Trellis Coded Modulation (TCM) with hybrid optimization in LMMSE has been provided. Moth amalgamated Elephant Herding Optimization (MAEHO) algorithm is the proposed hybrid optimization. Finally, the performance of the accepted model is determined in comparison to other existing techniques using a variety of metrics.