Multiple Input Research Articles

Evaluating the performance of intercity road freight transport: Double-frontier parallel network cross-efficiency model

Performance assessment of Intercity Road Freight Transport (IRFT) involve a complex set of considerations to ensure the efficient transportation of goods between their origins and destinations. Notably, Data Envelopment Analysis (DEA) models are often the preferred approach for conducting assessment studies that involve multiple inputs and outputs. Even though the conventional Cross-Efficiency (CE) technique addresses the limitations of DEA in producing comparable results, it still fails to consider the evaluation of internal system operations. The use of Network CE leads to more realistic results in assessing problems. It addresses not only the limitations of the DEA model in producing comparable results but also considers the performance of internal operations and system efficiency. Incorporating CE into a Parallel Network System (PNS), which is known as CE-PNS, has also a significant drawback as it neglects both the optimistic and pessimistic viewpoints based on the efficient and anti-efficient frontiers simultaneously, and may therefore fail to provide a comprehensive analysis. The primary objective of this study is to propose a new double-frontier network CE that takes into account two perspectives simultaneously. Indeed, this study is the first to incorporate the concepts of CE and double-frontier DEA into a PNS to provide an effective tool for assessing Iranian IRFT. For this purpose, the Cross-Inefficiency (CIE) technique is first incorporated into the PNS, called CIE-PNS, which explores the relationship between system inefficiency and internal divisions. The CE-PNS and CIE-PNS may yield different results. Therefore, Double-Frontier CE for a PNS (DFCE-PNS) is derived by combining the efficiency results obtained from CE-PNS and CIE-PNS. This study also examines different inputs and outputs to assess the performance of Iranian IRFT. The results demonstrate that DFCE-PNS yields more comprehensive and reliable results.

Advancements in MIMO Antennas for Enhanced Performance in 5G Smartphones Communication

In this review paper provides a comprehensive overview of recent advancements in multiple input multiple output (MIMO) antenna designs for 5G smartphones. There are various antenna configurations that are discussed along with benefit and limitations. The paper also examines key challenges such as antenna mutual coupling, isolation and the size constraints inherent in smartphones. Furthermore, the impact of MIMO antenna design on 5G smartphone performance characteristics such as gain, radiation efficiency and spatial multiplexing capability is also analyzed. Finally, future research directions and potential innovations in MIMO antenna technology for 5G smartphones are identified to address emerging requirements and opportunities in next-generation wireless communication systems. The comprehensive comparison of different MIMO antenna performance is also given.

A Multipath Approach to Self-Interference Cancellation in MIMO Rayleigh Fading Channels

An optically enabled multipath self-interference (MSI) cancellation technique for in-band full duplex (IBFD) multiple input multiple output (MIMO) Radio over Fiber RoF systems under Rayleigh fading channels is proposed. A digitally assisted Rayleigh fading channel model generates the MSI signal. An optically tunable multipath delay lines (OTMDL) module is designed using optical delay lines and an optical attenuator for coarse and fine-tuning of time delays and amplitude matching between the locally generated reference (LR) signal and the MSI signal. The OTMDL module adjusts the LR signal to cancel the effect of MSI on the signal received through the fading channel. The concept is validated using a remote sensor node of a 3 × 3 IBFD MIMO RoF system with a Rayleigh fading channel. The system's performance is evaluated by determining the MSI cancellation depths using the OTMDL section for various cancellation channels and by computing the MSI cancellation for both single band and wideband signals across different cancellation channels. An average value of MSI cancellation depth of 20.38 dB is obtained for a single-tone radio frequency signal in the range 15–25 GHz. Thus, the proposed system for MSI cancellation can be utilized in wireless communication systems in multiple ways.

Circularly polarized dielectric resonator based MIMO antenna for sub-6 GHz 5G cognitive radio applications

A 2-port wideband circular polarized (CP) multiple input multiple output (MIMO) dielectric resonator antenna (DRA) integrated with cognitive radio (CR) application is designed and investigated. This is a novel concept of integrating wideband CP-MIMO antenna based on dielectric resonator (DR), with CR concept. The multi-functional reconfigurable filter is designed within the feedline of the antenna making it applicable for both the operations of CR, namely, underlay and interweave. Rogers RT Duroid 5880 is used as the substrate for the designed CP-MIMO antenna. The antenna shows wide Axial Ratio (AR) bandwidth of 48.14% (2.2–3.50 GHz) and an overlapping bandwidth of 41.85% (2.37–3.50 GHz). The DR based MIMO antenna shows sensing range of 2.37–3.66 GHz. For the interweave operation, the designed CP-MIMO antenna shows narrowband frequency reconfigurability from 2.45–2.83 GHz. For the underlay case, the designed antenna shows band notch tunability between 3.23–3.62 GHz. The proposed antenna can be used effectively in sub-6 GHz band of the 5G communication systems.

Multiple regulatory inputs including cell envelope stress orchestrate expression of the Escherichia coli rpoN operon.

The rpoN operon, an important regulatory hub in Enterobacteriaceae, includes rpoN encoding sigma factor σ54, hpf involved in ribosome hibernation, rapZ regulating glucosamine-6-phosphate levels, and two genes encoding proteins of the nitrogen-related phosphotransferase system. Little is known about regulatory mechanisms controlling the abundance of these proteins. This study employs transposon mutagenesis and chemical screens to dissect the complex expression of the rpoN operon. We find that envelope stress conditions trigger read-through transcription into the rpoN operon from a promoter located upstream of the preceding lptA-lptB locus. This promoter is controlled by the envelope stress sigma factor E and response regulator PhoP is required for its full response to a subset of stress signals. σE also stimulates ptsN-rapZ-npr expression using an element downstream of rpoN, presumably by interfering with mRNA processing by RNase E. Additionally, we identify a novel promoter in the 3' end of rpoN that directs transcription of the distal genes in response to ethanol. Finally, we show that translation of hpf and ptsN is individually regulated by the RNA chaperone Hfq, perhaps involving small RNAs. Collectively, our work demonstrates that the rpoN operon is subject to complex regulation, integrating signals related to envelope stress and carbon source quality.

The multidimensional assessment of body representation and interoception in multiple sclerosis

BackgroundThe mental representation of the body (or body representation, BR) derives from the processing of multiple sensory and motor inputs and plays a crucial role in guiding our actions and in how we perceive our body. Fundamental inputs for BR construction come also from the interoceptive systems which refer to the whole bidirectional processes between the brain and the body. People with Multiple sclerosis (MS) show an abnormal multisensory integration which may compromise BR and interoception integrity. However, no study has evaluated possible deficits on distinct and dissociable dimensions of body representation (i.e., action-oriented, aBR; and a nonaction-oriented body representation, NaBR) and interoception (i.e., interoceptive accuracy, interoceptive sensibility, and interoceptive awareness) in MS. ObjectiveIn the present study, we aimed to determine whether participants with MS present changes in BR and interoceptive dimensions. MethodsWe performed comparison analyses on tasks and questionnaires tapping all BR and interoceptive dimensions between 36 people with relapsing-remitting MS (RRMS) and 42 healthy controls, and between 23 people with progressive MS (PMS) and 33 healthy controls. ResultsOverall, patients with MS exhibited lower interoceptive accuracy than matched controls. The RRMS group also showed higher visceral interoceptive sensibility levels. No differences were found in BR accuracy measures, but the PMS reported longer response times when performing the aBR task. ConclusionThese findings open a new issue on the role of inner‐signal monitoring in the body symptomatology of MS and highlight the need for an accurate BR and interoceptive assessment in a clinical setting.

Designing of CCII-Based Fractional Order Inverse Chebyshev Filters Using Gray Wolf Optimization Approach

The paper introduces a new approach for designing fractional-order inverse Chebyshev low-pass filters (FOICLF), with an emphasis on filters with an order of 1+α. The coefficients for these filters are determined through Gray Wolf Optimization by approximating filter response with the ideal response of same order FOICLF. Stability tests are conducted, and filters are implemented using Multiple Input Single Output topology with commercial ICs. Comparative analysis reveals close proximity between the ideal response of FOICLF and the designed filters, featuring maximum and mean square errors of –22.48 dB and –61.07 dB, respectively, for 1.7 order. Results are benchmarked against existing literature, highlighting superior performance in stopband and passband magnitude errors and stopband attenuation. Process, Voltage, and Temperature analysis assesses the impact of the parameter variations on the –3 dB frequency, assuring the robustness of designed filters with relative standard deviations ranging from 0.003% to 3.013% for the 1.7 order filter.

Multiplicative Basis Function Based Adaptive DOA Estimation in Optimal MIMO Sparse Antenna Reconfiguration Model

Direction of arrival (DOA) estimation using sparse signal representation has gained significant attention in recent decades. The spatial signals utilized for DOA estimation are reconstructed through a novel compressive sensing (CS) approach. In this study, we used a CS approach to ascertain the adaptive underdetermined DOA for multiple inputs and multiple output (MIMO) sparse media. Accurate DOA estimation through the CS approach contributes to enhanced signal investigation and the suppression of undesired noise in a sparse channel. The method proposed herein encompasses the development and execution of a novel multiplicative basis function known as the multi-kernel supported non-negative sparse Bayesian learning (NNSBL) algorithm, which is implemented on predefined grid values. Concurrently, stochastic grey wolf optimization (GWO) is virtually applied to increase the degrees of freedom (DOF) on a minimum redundancy array (MRA) while considering an optimal antenna reconfiguration model. The advantages of this proposed approach include the generation of a distinct manifold matrix for precise beam steering, resulting in improved DOA estimation accuracy. Additionally, an augmentation of DOF using GWO was observed, with low computational complexity. The simulated result of proposed algorithm leads to the attainment of optimal minimum root mean square error (RMSE) across various optimal wavelengths of the stochastic sources. Finally, a comparative analysis of RMSE and convergence plots confirms the superior performance and high potentiality of the proposed method in comparison to existing techniques across different SNR values. Notably, there is significant reduction in RMSE which is suitable for precise DOA estimation in signal processing applications.

Analysis of regional carbon productivity differences and influencing factors—based on new green decomposition model

This paper introduces a new green decomposition model of carbon productivity that aims to further analyze the regional differences in carbon productivity and its interaction with regional industrial performance. We combine desired outputs and undesired outputs orientation, and multiple factor inputs to derive a new green decomposition theorem, establish a new green decomposition model of carbon productivity, and obtain nine effects of regional carbon productivity differences. Empirical analysis is conducted using input-output data from 29 provinces and 15 industries in China, comparing the differences in carbon productivity from both the provincial and industry perspectives and exploring the mechanism of action. This paper provides theoretical basis and empirical evidence for regional carbon productivity enhancement and economic and industrial optimization from the perspective of multi-factor inputs, as well as policy insights for regional low-carbon transition development.

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments.

Novel Waveform Design with a Reduced Cyclic Prefix in MIMO Systems

For well-known orthogonal frequency division multiplexing (OFDM), the cyclic prefix (CP) is essential for coping with multipath channels. Nevertheless, CP is a pure redundant signal, which wastes valuable time–frequency resources. We propose a novel waveform based on symbol repetition, which is presented to cut down the CP overhead in OFDM. In the presented OFDM with symbol repetition (SR-OFDM), one CP is inserted in the front of several transmitted symbols, instead of only one symbol, as in the conventional way. As a result, it can save the overhead created by CP. Furthermore, due to the existence of the remaining CP, the multipath channel can still be converted into the frequency domain, and single-tap equalization can still be used to equalize information free from interference. In addition, we also extend the proposed SR-OFDM into multiple input–multiple output (MIMO) systems. Finally, the proposed schemes are validated by computer simulations under the various channels.

FSS inspired two port CP MIMO antenna with enhanced gain for X-band applications

In this work, a dual-port circularly polarized (CP) Multiple Input Multiple Output (MIMO) antenna equipped with frequency selective surface (FSS) for X-band applications is designed and analyzed. The antenna system aims to enhance gain, a critical parameter like isolation radiation pattern, etc. In wireless communication systems working in the X-band frequency range. The present MIMO antenna consists of two parts that provide coverage for the whole frequency band of 7.6–11.6 GHz, specifically for X-band communication applications. As a decoupled network, the meander line architecture greatly improves isolation. The MIMO antenna's measurements include 25 × 30 × 1 mm3. To improve the antenna's peak gain even further, a FSS is placed below the present MIMO antenna at 10.5 mm. The whole volume of the FSS-inspired composite structure is 50 × 60 × 11.6 mm3. By correctly incorporating the FSS array with the antenna's structure and utilizing its frequency-selective properties, gain improvement can be attained. Without FSS, the antenna's gain is 4.8 dBi and it is enhanced by 21 % and the maximum value of gain is 15 dBi in the desired frequency spectrum. The efficiency of the proposed FSS-inspired structure is >84.2% in the operating frequency band. To evaluate the MIMO performance of the current antenna, several attributes are evaluated. On the evaluation, it is found that the present MIMO antenna has an envelope correlation coefficient (ECC) < 0.004, a diversity gain (DG) over 9.99, measured mean effective gain (MEG) below 3 dB, and CCL of less than 0.4 bits/s/Hz throughout the resonant frequency spectrum. Through modeling and experimental testing, the current MIMO antenna model is created and verified.

Fractional-Order Least-Mean-Square-Based Active Control for an Electro–Hydraulic Composite Engine Mounts

For the vibration of automobile powertrain, this paper designs electro–hydraulic composite engine mounts. Subsequently, the dynamic characteristics of the hydraulic mount and the electromagnetic actuator were analyzed and experimentally studied separately. Due to the strong nonlinearity of the hybrid electromechanical engine mount, a Fractional-Order Least-Mean-Square (FGO-LMS) algorithm was proposed to model its secondary path identification. To validate the vibration reduction effect, a rapid control prototype test platform was established, and vibration active control experiments were conducted based on the Multiple–Input Multiple–Output Filter-x Least-Mean-Square (MIMO-FxLMS) algorithm. The results indicate that, under various operating conditions, the vibration transmitted to the chassis from the powertrain was significantly suppressed.

Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data

In remote sensing, multiple input bands are derived from various sensors covering different regions of the electromagnetic spectrum. Each spectral band plays a unique role in land use/land cover characterization. For example, while integrating multiple sensors for predicting aboveground biomass (AGB) is important for achieving high accuracy, reducing the dataset size by eliminating redundant and irrelevant spectral features is essential for enhancing the performance of machine learning algorithms. This accelerates the learning process, thereby developing simpler and more efficient models. Our results indicate that compared individual sensor datasets, the random forest (RF) classification approach using recursive feature elimination (RFE) increased the accuracy based on F score by 82.86 % and 26.19 respectively. The mutual information regression (MIR) method shows a slight increase in accuracy when considering individual sensor datasets, but its accuracy decreases when all features are taken into account for all models. Overall, the combination of features from the Landsat 8, ALOS PALSAR backscatter, and elevation data selected based on RFE provided the best AGB estimation for the RF and XGBoost models. In contrast to the k-nearest neighbors (KNN) and support vector machines (SVM), no significant improvement in AGB estimation was detected even when RFE and MIR were used. The effect of parameter optimization was found to be more significant for RF than for all the other methods. The AGB maps show patterns of AGB estimates consistent with those of the reference dataset. This study shows how prediction errors can be minimized based on feature selection using different ML classifiers.

Enhancing gain and isolation of a quad-element MIMO antenna array design for 5G sub-6 GHz applications assisted with characteristic mode analysis

This paper presents a novel quad-element array with multiple inputs and multiple outputs (MIMO) designed for 5th generation sub-6 GHz applications. The MIMO system achieves a wide impedance bandwidth, high gain, and high isolation among its components, representing significant advancements in sub-6 GHz antenna applications. The single element, an elliptical resonator with a circular slot, is fed by a 50 Ω microstrip feedline, achieves a broad characteristic bandwidth from 3.7 to 5.7 GHz with a resonant frequency of 4.33 GHz and a gain of 1.81 dBi. Characteristic Mode Analysis (CMA) was employed to elucidate the evolution phases of this design. The quad-element MIMO antenna array maintains a compact size and broadband characteristics by arranging mirrored elements on the same ground plane. Implemented on a cost-effective FR-4 substrate measuring 44 × 44 × 1.6 mm3, the recommended MIMO antenna array, enhanced with a partial ground plane and due to the introduction of a vertical strip, a high isolation of − 38.53 dB is achieved between MIMO components along with a realized gain of 3.01 dBi and a radiation efficiency of 71% in the 5G sub-6 GHz band. Noteworthy properties include high isolation, diversity gain (DG), and envelope correlation coefficient (ECC), verifying the appropriateness of the suggested MIMO scheme for 5G transmission and reception in sub-6 GHz applications.

A Compact GaN Power Amplifier Module for New Generation Cellular Basestations

This paper presents a compact class-AB Power Amplifier Module (PAM) designed for new generation massive Multiple Input Multiple Output (MiMo) cellular base stations. The module is designed at the center frequency of 3.5GHz targeting Long Term Evolution (LTE) and 5G New Radio (NR) bands. The module is a hybrid design that incorporates a Gallium Nitride (GaN) High Mobility-Electron Transistor (HEMT) die, discrete components-based input, and output matching networks. The entire design is realized on an 8.5 x 5.2 mm, 2-layer Rogers4003C substrate. The module is assembled on a PCB as an open-top for post-characterization tuning. The small signal and large signal measurements are in quite good agreement. The measurement results show that the amplifier is unconditionally stable, the input return loss is 12.2 dB, the output return loss is 7.7 dB, and the small signal gain is 13.4 dB. The saturated output power is 33.3 dBm with a Power Added Efficiency of 20.1%. The small signal gain drops to 11.2 dB at around 22 dBm of input power due to GaN technology’s intrinsic soft compression characteristics.

Development of multiple input supply based modified SEPIC DC–DC converter for efficient management of DC microgrid

The development of DC microgrids is reliant on multi-input converters, which offer several advantages, including enhanced DC power generation and consumption efficiency, simplified quality, and stability. This paper describes the development of a multiple input supply based modified SEPIC DC–DC Converter for efficient management of DC microgrid that is powered by two DC sources. Here Multi-Input SEPIC converter offers both versatility in handling output voltage ranges and efficiency in power flow, even under challenging operating conditions like lower duty cycle values. These features contribute to the converter's effectiveness in managing power within a DC microgrid. In this configuration, the DC sources can supply energy to the load together or separately, depending on how the power switches operate. The detailed working states with equivalent circuit diagrams and theoretical waveforms, under steady-state conditions, are shown along with the current direction equations. This paper also demonstrates the typical analysis of large-signal, small-signal, steady-state modeling techniques and detailed design equations. The proposed configuration is validated through the conceptual examination using theoretical and comprehensive MATLAB simulation results. Detailed performance analysis has been done for different cases with various duty ratios. Finally, to show the competitiveness, the multi-input SEPIC topology is compared with similar recent converters.

Sample-imbalanced wafer map defects classification based on auxiliary classifier denoising diffusion probability model

This paper research on the classification task of wafer map defects under imbalanced sample. To improve the number of imbalanced wafer maps, a denoising diffusion probabilistic model with auxiliary classifier is proposed to generate new wafer maps. The developed model has an U-net structure with multiple inputs and outputs, taking wafer images, noise degree and category features as inputs, and outputs predicted noise and prediction category. When performing data augmentation, the trained model gradually removes prediction noise from the initial random noise and obtains new wafers. Finally, the balanced wafer defect maps are classified using a residual neural network. The proposed auxiliary classifier denoising diffusion probabilistic model and residual neural networks (ACDDPM-ResNet) method has validated on the MIR-WM811K dataset and MixedWM38 dataset, and the defect classification results of the imbalanced wafer map are remarkable improved after data augmentation. In addition, the paper also discusses and analyzes the influence of the maximum noise addition steps and the data augmentation size on the accuracy of wafer classification. It is further validated that the proposed wafer map classification method based on data augmentation can solve the classification problem caused by sample imbalance.

Flow Field Estimation with Distortion Correction Based on Multiple Input Deep Convolutional Neural Networks and Hartmann–Shack Wavefront Sensing

The precise estimation of fluid motion is critical across various fields, including aerodynamics, hydrodynamics, and industrial fluid mechanics. However, refraction at complex interfaces in the light path can cause image deterioration and lead to severe measurement errors if the aberration changes with time, e.g., at fluctuating air–water interfaces. This challenge is particularly pronounced in technical energy conversion processes such as bubble formation in electrolysis, droplet formation in fuel cells, or film flows. In this paper, a flow field estimation algorithm that can perform the aberration correction function is proposed, which integrates the flow field distribution estimation algorithm based on the Particle Image Velocimetry (PIV) technique and the novel actuator-free adaptive optics technique. Two different multi-input convolutional neural network (CNN) structures are established, with two frames of distorted PIV images and measured wavefront distortion information as inputs. The corrected flow field results are directly output, which are divided into two types based on different network structures: dense estimation and sparse estimation. Based on a series of models, a corresponding dataset synthesis model is established to generate training datasets. Finally, the algorithm performance is evaluated from different perspectives. Compared with traditional algorithms, the two proposed algorithms achieves reductions in the root mean square value of velocity residual error by 84% and 89%, respectively. By integrating both flow field measurement and novel adaptive optics technique into deep CNNs, this method lays a foundation for future research aimed at exploring more intricate distortion phenomena in flow field measurement.

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.