Multiple Input Research Articles

MIMO Detection using Optimized Neural Network model for 5G Communication

Multiple Input Multiple Output (MIMO) Wireless communication uses several antennas in the transmitter and receiver end. Multiple antennas at each end of the communication circuit work together to reduce errors, increase data speed, and improve radio transmission efficiency. MIMO techniques, which increases the number of antennas at the base station, will be used in 5G. Interference between users, which reduces a wireless link's spectral efficiency (SE), poses a major challenge during uplink communications with mm-wave systems. A linear MIMO detector such as maximum ratio (MR) or minimum mean square error (MMSE) cannot resolve this phenomenon. Therefore, this paper proposes a MIMO detection method based on an optimised extreme learning machine neural network (OELMNN). The Adaptive Rat Optimization algorithm is used to optimise the weight and bias values of ELMNN to improve its performance. The proposed scheme's performance is measured in terms of SNR, MSE, BER, and spectral efficiency.

Modeling and simulation of a multi source microwave heating of soil based on PSO-BPNN

Due to rapid industrialization, accumulation of organic pollutants in the soil has been an increasing menace, having detrimental effect on crop yields, toxicity to flora and fauna, and environment in total. Microwave soil remediation technology, has garnered a significant attention in the field of thermal degradation of organic-polluted soil. However, the high-temperature remediation often involve the use of microwave devices with multiple sources, leading to competing and coupling effects, significantly impacting heating efficiency. Therefore, an attempt was made to develop a multiple source microwave device with adjustable inputs for soil heating, to design adjustment schemes for multiple input units. A multi-physics field model consistent with the testing apparatus was developed and utilized to generate the temperature for 625 different adjustment schemes. To further refine and to optimize the adjustment schemes a PSO-BPNN model was integrated and utilized. The simulation results were utilized to predict the most effective adjustment scheme, and was tested. The model potentially serves to estimate the optimal adjustment scheme for multiple source microwave inputs for effective heating of soil.

EEG Cortical Sources of Theta, Beta and Gamma Frequencies Orchestrate Audio-Visual Interactions.

The fascinating ability of brain to integrate information from multiple sensory inputs has intrigued many researchers. Audio-visual (AV) interaction is a form of multisensory integration which we encounter to form meaningful representations of the environment around us. There is limited literature related to the underlying neural mechanisms. Quantitative EEG (QEEG), a tool with high temporal resolution can be used to understand cortical sources of AV interactions. EEG data was recorded using 128 channels from 30 healthy subjects using audio, visual and AV stimuli in 'object detection task'. Electrical source imaging was performed using s-LORETA across seven frequency bands (lower alpha 1, lower alpha 2, upper alpha, beta, delta, gamma, theta) during AV versus unimodal conditions across 66 gyri. The cortical sources were activated in the theta, beta, and gamma bands in cross modal versus unimodal conditions, which we propose, reflect neural communication for AV interaction network. The cortical sources constituted areas involved with visual processing, auditory processing, established multisensory (frontotemporal cortex, parietal cortex, middle temporal gyrus, superior frontal gyrus, inferior frontal gyrus, precentral gyrus) and potential multisensory areas (paracentral, postcentral and subcallosal). Together, these results offer an integrative view of cortical areas in frequency oscillations during AV interactions.

Innovative resource allocation mechanism for optimizing 5G multi user‐massive multiple input multiple output system

Abstract5G networks are essential in all locations owing to the multitude of advantages they provide. As a result, the number of users has increased dramatically. Nevertheless, these users require a variety of resources in order to function efficiently. Deep learning techniques have been created to improve the precision and dependability of resource allocation in the context of 5G networks. This research utilizes an efficient recurrent neural network (ERNN) to handle resource allocation for 5G multiuser (MU)‐massive multiple input multiple output (MIMO). In order to optimize the objective functions, the first application of the multi‐objective differential evaluation algorithm (MODEA) is used. The neural network is provided with these updated goal functions in order to allocate resources. ERNN evaluates the level of need for each individual user. By partitioning the resource at this level, it maintains a high throughput while distributing it to each user. In addition, the fairness index of the resource distribution system based on neural networks is established. The suggested method achieves a data transfer rate of 290 bits per second (bps) and a fairness index of 0.97% when used by 50 users. The findings of the proposed method exhibit superior performance compared to other existing methods in the field of 5G massive MIMO.

Programmable Assembly of Semiconductor Mesostructures Via Inorganic Phototropism

Plants exhibit a phototropic response and direct the addition of new biomass to optimize collection of solar insolation. Analogous inorganic phototropism growth enables the programmable assembly of complex 3D nanoarchitectures with instruction by an incoherent, unstructured, mW cm-2 intensity light beam. Inorganic phototropism has been demonstrated via the light-mediated electrochemical assembly of semiconductor deposits from solution-phase precursor ions. No structured light field (no photomask), no lithographic processes and no templating agents (ligands, surfactants, etc.) are utilized. Nevertheless, ordered nanoscale features are conformally assembled over full macroscale (cm2) areas with feature heights on the order of several um with growth times < 5 min. In-plane anisotropy is a function of the input polarization. Isotropic morphologies consisting of ordered arrays of nanoscale holes were generated using unpolarized illumination whereas linearly polarized light resulted in anisotropic lamellar structures with in-plane orientations set by the polarization direction. The structure pitch was dependent on the spectral distribution of the input light with shorter wavelengths effecting higher feature densities. The out-of-plane growth direction is related to the propagation direction of the input light. Time-varying optical inputs enable programming of 3D intricacy by evolving the in-plane structure along the out-of-plane dimension, e.g. an abrupt reduction in the input wavelength results in a concomitant increase in the interfacial density resulting in tuning fork structures. Additional morphological control has also been effected by using multiple simultaneous illumination inputs. Modeling of the growth using a combination of full-wave electromagnetic simulations of light absorption and scattering coupled with Monte Carlo simulations of mass addition successfully reproduced the experimentally observed morphologies and indicated that assembly was directed by evolution of the growth front to maximize anisotropic light collection. The assembly was observed to be a highly emergent phenomenon involving optical communication between neighboring features including cooperative scattering and synergistic absorption.

Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles

Artificial neuromorphic devices can emulate dendric integration, axonal parallel transmission, along with superior energy efficiency in facilitating efficient information processing, offering enormous potential for wearable electronics. However, integrating such circuits into textiles to achieve biomimetic information perception, processing, and control motion feedback remains a formidable challenge. Here, we engineer a quasi-solid-state iontronic synapse fiber (ISF) comprising photoresponsive TiO2, ion storage Co-MoS2, and an ion transport layer. The resulting ISF achieves inherent short-term synaptic plasticity, femtojoule-range energy consumption, and the ability to transduce chemical/optical signals. Multiple ISFs are interwoven into a synthetic neural fabric, allowing the simultaneous propagation of distinct optical signals for transmitting parallel information. Importantly, IFSs with multiple input electrodes exhibit spatiotemporal information integration. As a proof of concept, a textile-based multiplexing neuromorphic sensorimotor system is constructed to connect synaptic fibers with artificial fiber muscles, enabling preneuronal sensing information integration, parallel transmission, and postneuronal information output to control the coordinated motor of fiber muscles. The proposed fiber system holds enormous promise in wearable electronics, soft robotics, and biomedical engineering.

Analysis of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing with Dynamic Optimal Power Allocation

Analysis of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing with Dynamic Optimal Power Allocation

An asymmetrically variable wingtip anhedral angles morphing aircraft based on incremental sliding mode control: Improving lateral maneuver capability

This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft, inspired by biomimetic mechanisms, to enhance lateral maneuver capability. Firstly, we establish a lateral dynamic model considering additional forces and moments resulting during the morphing process, and convert it into a Multiple Input Multiple Output (MIMO) virtual control system by importing virtual inputs. Secondly, a classical dynamics inversion controller is designed for the outer-loop system. A new Global Fast Terminal Incremental Sliding Mode Controller (NDO-GFTISMC) is proposed for the inner-loop system, in which an adaptive law is implemented to weaken control surface chattering, and a Nonlinear Disturbance Observer (NDO) is integrated to compensate for unknown disturbances. The whole control system is proven semi-globally uniformly ultimately bounded based on the multi-Lyapunov function method. Furthermore, we consider tracking errors and self-characteristics of actuators, a quadratic programming-based dynamic control allocation law is designed, which allocates virtual control inputs to the asymmetrically deformed wingtip and rudder. Actuator dynamic models are incorporated to ensure physical realizability of designed allocation law. Finally, comparative experimental results validate the effectiveness of the designed control system and control allocation law. The NDO-GFTISMC features faster convergence, stronger robustness, and 81.25% and 75.0% reduction in maximum state tracking error under uncertainty compared to the Incremental Nonlinear Dynamic Inversion Controller based on NDO (NDO-INDI) and Incremental Sliding Mode Controller based on NDO (NDO-ISMC), respectively. The design of the morphing aircraft significantly enhances lateral maneuver capability, maintaining a substantial control margin during lateral maneuvering, reducing the burden of the rudder surface, and effectively solving the actuator saturation problem of traditional aircraft during lateral maneuvering.

A novel BP algorithm‐based UWB MIMO ground‐penetrating imaging system

AbstractUltra‐wideband (UWB) multiple input multiple output (MIMO) radar has been widely used in detection systems due to its advantages, such as low cost and high data acquisition capability. The detection system is applied in various ground‐penetrating applications, including mine detection and earthquake rescue. In this paper, a novel UWB MIMO radar system based on step‐frequency continuous wave is designed for underground target detection. To improve the penetration depth and high‐range resolution, a new miniature Vivaldi antenna with a bandwidth of 1–3 GHz is designed and integrated into the ground‐penetrating imaging system. Furthermore, a multiscale weighted time‐domain back projection (BP) algorithm is proposed to suppress artifacts and reduce the computational complexity of the BP algorithm. The designed system is tested by detecting stationary targets. The experimental results agree well with the simulation. The reconstructed images show that the designed MIMO radar system can successfully image underground targets.

NOMA‐based precoded quadrature spatial modulation in multiuser MIMO downlink transmission over correlated channel

SummaryIn this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme.

Research on personal credit scoring model based on deep learning

The increasing integration of internet technology and the financial industry has led to the gradual replacement of traditional credit evaluation models with those based on deep learning, which have demonstrated excellent accuracy. This has become a prominent area of research. Nevertheless, the credit scoring model based on a deep neural network encounters significant challenges in terms of its applicability in the field of credit scoring, largely due to the opaque nature of its learning and decision-making processes. The application of deep learning to personal credit scoring has been shown to enhance the accuracy of the resulting scores by leveraging large amounts of data. The model employs a deep neural network (DNN) architecture that integrates multiple input features, including the user's transaction history, social behaviour and other relevant data. The model is trained using supervised learning, with a large amount of labelled data used to optimise its prediction performance. Experimental results demonstrate that the deep learning-based model exhibits a notable improvement in accuracy and robustness compared to traditional credit scoring models.

Enhancing Vapor Compression Refrigeration Systems Efficiency via Two-Phase Length and Superheat Evaporator MIMO Control

The present investigation focuses on enhancing the efficiency of a vapor compression refrigeration system (VCRS) by proposing a Multiple Input Multiple Output (MIMO) control strategy based on the evaporator’s two-phase length and superheat temperature. A moving boundary dynamic model for the VCRS is implemented using the Thermosys Matlab Toolbox. The study analyzes the influence of actuation parameters, specifically compressor speed and expansion valve opening, on control parameters, namely two-phase length and superheat temperature. A comprehensive analysis based on the first and second laws of thermodynamics is conducted across a wide range of operating conditions. Simulation results demonstrate that two-phase length can be effectively utilized as a control parameter by selecting operating points that maximize the system efficiency. Additionally, the study reveals that extending the evaporator’s two-phase length to 80–90% of its limit increases system efficiency, enabling a reduction in compressor speed while maintaining the cooling capacity.

START: A Versatile Platform for Bacterial Ligand Sensing with Programmable Performances.

Recognition of signaling molecules for coordinated regulation of target genes is a fundamental process for biological systems. Cells often rely on transcription factors to accomplish these intricate tasks, yet the subtle conformational changes of protein structures, coupled with the complexity of intertwined protein interaction networks, pose challenges for repurposing these for bioengineering applications. This study introduces a novel platform for ligand-responsive gene regulation, termed START (Synthetic Trans-Acting Riboswitch with Triggering RNA). Inspired by the bacterial ligand sensing system, riboswitch, and the synthetic gene regulator, toehold switch, the START platform enables the implementation of synthetic biosensors for various ligands. Rational sequence design with targeted domain optimization yields high-performance STARTs with a dynamic range up to 67.29-fold and a tunable ligand sensitivity, providing a simple and intuitive strategy for sensor engineering. The START platform also exhibits modularity and composability to allow flexible genetic circuit construction, enabling seamless implementation of OR, AND, and NOT Boolean logic gates for multiple ligand inputs. The START design principle is capable of broadening the suite of synthetic biosensors for diverse chemical and protein ligands, providing a novel riboregulator chassis for synthetic biology and bioengineering applications.

MFTM-Informer: A multi-step prediction model based on multivariate fuzzy trend matching and Informer

Multi-step forecasting is a critical process in various fields, such as disaster warning and financial analysis. Nevertheless, achieving precise multi-step forecasting is challenging due to the intricate nature of the factors influencing the time series, most of which are highly nonlinear and nonstationary. In this paper, a multi-step forecasting model named MFTM-Informer, employing a multiple input multiple output strategy for multivariate trend matching is proposed. The dependent variable and the influencing factors are initially decomposed using multivariate variational modal decomposition to minimize noise. Afterwards, the decomposed data are reconstructed into multivariate trends and fluctuations using sample entropy, enabling the development of tailored forecasting strategies based on data characteristics. A multivariate trend is predicted using an enhanced pattern matching model, while the high-frequency fluctuation is modelled using Informer. Finally, the outcomes are combined to generate multi-step predictions. To validate the performance of the proposed model, we observed its performance on three real-world datasets, including Brent crude oil prices, European Union Allowance future prices, and Standard & Poor’s 500 index. Results indicate that the model surpasses all the benchmark models in terms of multiple evaluation metrics and forecast ranges, highlighting its effectiveness and robustness in multi-step forecasting.

Improving children’s learning in developing countries: what works and what doesn’t

Policymakers in low- and middle-income countries have been successful in getting children to school. However, spending more time in school has not led to the development of age-appropriate math and language skills. In contrast to earlier full-length systematic reviews, we will briefly summarize key findings and challenges from the education literature in development economics targeting this article towards non-specialists. Two findings emerge from our review: one, given the complementarities in educational inputs, we need policy interventions that target multiple inputs in a child production function to have the greatest impact on learning outcomes. Two, interventions that target teacher effort and student effort together appear to have the greatest promise in being able to facilitate learning among children.

Model-based sparse optimal control of the hydrogen sulfide synthesis process for acidic wastewater sulfidation

Hydrogen sulfide is one of the commonly used materials of the gas-liquid sulfidation process for treating nonferrous metallurgical acidic wastewater. Using hydrogen and sulfur to synthesize hydrogen sulfide can obtain hydrogen sulfide with higher purity and at a lower cost. Due to the complex multiphase reaction mechanism of the hydrogen sulfide synthesis (HSS) process and production safety conditions, the manual operation requires a long time to ensure the continued stability of the system under high temperatures and high pressure. In this paper, a dynamic model and a novel sparse control strategy of the HSS process for acidic wastewater sulfidation are proposed to enable the HSS process operates continuously and safely. Firstly, a multivariate and multiphase mechanism model with multiple inputs and outputs of the HSS process is established. Subsequently, the novel sparse feedback matrix is designed under the constraint of the fixed l0-norm and the solvability of the eigenvalue determinant equation. The sparse feedback matrix solutions are optimized by pole placement and dynamic programming for continuous and discrete systems, respectively. Finally, the effectiveness of the HSS model is validated with industrial data. The superiority of the proposed model-based control strategy is demonstrated in the HSS system compared with conventional control methods.

Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system

This study explores two scenarios for optimizing the predictive control of flux in water desalination systems: experimental design of direct contact membrane distillation (DCMD) and artificial intelligence (AI) models. Deep learning networks, specifically Long Short-Term Memory (LSTM), and standalone AI models, including hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), were evaluated. Evaluation criteria, along with 2D graphical visualization, were used to assess model accuracy. ANFIS-C4 emerged as the standout model, achieving perfect predictive skills with 100 % accuracy and low RMSE of 0.0522 in the training phase, and maintaining high performance in testing with 99.73 % accuracy and RMSE of 0.7121. Similarly, ANN-C4 demonstrated near-perfect accuracy with 100 % accuracy and RMSE of 0.0643 in the training phase and also maintained excellent performance in the testing phase. Despite requiring multiple inputs, ANFIS and ANN show remarkably high capability in predictive accuracy. Among LSTM models, LSTM-C3 achieved the highest training DC of 91.35 %, indicating robust performance in capturing training data variability. LSTM-C2 showed superior generalization with a testing DC of 0.8539, despite using only two input parameters. The narrow distribution of predicted values around the median in violin plots for ANFIS-C4 and ANN-C4, along with their low RMSE, validates their superior performance. The exceptional results of ANFIS-C4 and ANN-C4 highlight their potential in water desalination applications, supporting Sustainable Development Goal 6 (SDGs-6) and Environmental Protection Agency (EPA) objectives for sustainable water management. This research underscores the importance of advanced computational models in enhancing the efficiency and reliability of water desalination systems, contributing to global efforts in sustainable water resource management.

Prediction of HPC compressive strength based on machine learning

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research.

Minimum peak sidelobe ratio filter for MIMO radar via convex optimization

Multiple input multiple output (MIMO) radar retains transmitting freedom by transmitting multiple waveforms simultaneously. However, due to the cross-correlation between waveforms, the performance of range sidelobes is degraded. So, low sidelobes techniques are especially demanded in MIMO radar. This article proposes a mismatched filtering method for minimizing MIMO radar’s peak sidelobe level ratio (PSLR). In this method, we formulate the problem as a convex optimization (CO) problem and thus ensure its solution is globally optimal. To apply mismatched filters to MIMO radar, we introduce the traditional strategy and propose a new one. Instead of attempting to minimize all auto-correlations and cross-correlations of all waveforms as in the traditional strategy, our proposed strategy directly optimizes the mismatched filter of the synthesis signal for each considered direction. To verify the effectiveness of our method, numerical simulations are carried out. Firstly, the PSLR effects of those two strategies are compared. The results indicate that our proposed strategy dramatically improves the PSLR performance of MIMO radar compared to the traditional one. Then, the influences of several parameters on the PLSR performance are revealed, which indicates that our method is flexible enough to make trade-offs between PSLR and other parameters.

Range-Velocity Measurement Accuracy Improvement Based on Joint Spatiotemporal Characteristics of Multi-Input Multi-Output Radar

For time division multiplexing multiple input multiple output (TDM MIMO) millimeter wave radar, the measurement of target range, velocity and other parameters depends on the phase of the received Intermediate Frequency (IF) signal. The coupling between range and velocity phases occurs when measuring moving targets, leading to inevitable errors in calculating range and velocity from the phase, which in turn affects measurement accuracy. Traditional two-dimensional fast fourier transform (2D FFT) estimation errors are particularly pronounced at high velocity, significantly impacting measurement accuracy. Additionally, due to limitations imposed by the Nyquist sampling theorem, there is a restricted range for velocity measurements that can result in aliasing. In this study, we propose a method to address the coupling of range and velocity based on the original signal as well as a method for velocity compensation to resolve aliasing issues. Our research findings demonstrate that this approach effectively reduces errors in measuring ranges and velocities of high-velocity moving targets while efficiently de-aliasing velocities.