Multiple Input Research Articles

A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems.

What does it mean for a complex system to "compute" or perform "computations"? Intuitively, we can understand complex "computation" as occurring when a system's state is a function of multiple inputs (potentially including its own past state). Here, we discuss how computational processes in complex systems can be generally studied using the concept of statistical synergy, which is information about an output that can only be learned when the joint state of all inputs is known. Building on prior work, we show that this approach naturally leads to a link between multivariate information theory and topics in causal inference, specifically, the phenomenon of causal colliders. We begin by showing how Berkson's paradox implies a higher-order, synergistic interaction between multidimensional inputs and outputs. We then discuss how causal structure learning can refine and orient analyses of synergies in empirical data, and when empirical synergies meaningfully reflect computation versus when they may be spurious. We end by proposing that this conceptual link between synergy, causal colliders, and computation can serve as a foundation on which to build a mathematically rich general theory of computation in complex systems.

Evaluating the Performance of Taiwan Airport Renovation Projects: An Application of Multiple Attributes Intelligent Decision Analysis

Performance evaluation is a vital tool for measuring whether construction projects meet their established objectives, particularly in complex tasks. It helps identify key areas for improvement and enhances resource allocation efficiency. Through precise performance evaluation, managers can make optimal decisions regarding resource use, ultimately increasing project productivity and overall performance. The objective of this study is to measure the production efficiency of airport renovation projects in Taiwan using data envelopment analysis (DEA) and to apply artificial neural networks (ANN) for predicting project quality. DEA effectively handles scenarios with multiple inputs and outputs, providing relative efficiency comparisons among projects and quantifying the potential for improvement. ANN, on the other hand, can learn nonlinear patterns from the data, allowing for accurate predictions of project quality. As construction projects become more complex, ensuring efficient operation within limited resources becomes increasingly crucial. Traditional performance evaluation methods are inadequate for addressing scenarios involving multiple inputs and outputs; therefore, using DEA and ANN offers a more accurate framework for analysis and prediction. The results of this study indicate that, through the DEA model, five projects achieved an efficiency score of 1, while twelve inefficient projects need to reduce defect frequency by 54.76% and increase the progress and budget implementation efficiency by an average of 10.33% to improve performance. The ANN model achieved a classification accuracy of 94.1% and a mean squared error (MSE) of 0.11 in regression predictions. By adopting a data-driven approach, ANN facilitates intelligent decision making and forecasting throughout the construction process. This study provides construction managers with concrete guidelines for resource allocation and quality prediction, thus enhancing project management effectiveness.

Compact high isolation MIMO antenna array for 6G application (0.1–10 THZ)

ABSTRACT In wireless communication, channel capacity and reliability can be increased using a Multiple Input Multiple Output antenna. For several future applications, the sixth generation terahertz frequency spectrum (0.1–10 THz) will be used to increase the communication speed in wireless networks. To minimise coupling and reconfiguration, the decoupling structure and pin diodes are used in the MIMO antenna. This research presents a four-port electromagnetic band gap-based MIMO antenna array for 6 G applications. The entire MIMO system is influenced by the electromagnetic coupling between the four or eight MIMO parts. In the ground plane, antenna size reduction, insulation improvement, and defective ground are used. Here, the antenna is designed and compared using the two substrates Flame Retardant and polyimide. The performance of the suggested design is 288 GHz Polyimide and 301.6 GHz FR4. The suggested design showed better performance of the antenna as well as diversity measures.

A Hybrid Network Integrating MHSA and 1D CNN–Bi-LSTM for Interference Mitigation in Faster-than-Nyquist MIMO Optical Wireless Communications

To mitigate inter-symbol interference (ISI) caused by Faster-than-Nyquist (FTN) technology in a multiple input multiple output (MIMO) optical wireless communication (OWC) system, we propose an ISI cancellation algorithm that combines multi-head self-attention (MHSA), a one-dimensional convolutional neural network (1D CNN), and bi-directional long short-term memory (Bi-LSTM). This hybrid network extracts data features using 1D CNN and captures sequential information with Bi-LSTM, while incorporating MHSA to comprehensively reduce ISI. We analyze the impact of antenna numbers, acceleration factors, wavelength, and turbulence intensity on the system’s bit error rate (BER) performance. Additionally, we compare the waveform graphs and amplitude–frequency characteristics of FTN signals before and after processing, specifically comparing sampled values of four-pulse-amplitude modulation (4PAM) signals with those obtained after ISI cancellation. The simulation results demonstrate that within the Mazo limit for selecting acceleration factors, our proposal achieves a 7 dB improvement in BER compared to the conventional systems without deep learning (DL)-based ISI cancellation algorithms. Furthermore, compared to systems employing a point-by-point elimination adaptive pre-equalization algorithm, our proposal exhibits comparable BER performance to orthogonal transmission systems while reducing computational complexity by 31.15%.

Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

Massive MIMO uplink and downlink joint representation based on couple dictionary learning

AbstractThe challenge of jointly representing both the uplink (UL) and downlink (DL) in massive multiple input multiple output (MIMO) systems have been tackled. Considering the angular reciprocity, a couple dictionary learning (CDL) support to enhance performance and address high complexity has been introduced. This approach minimizes the number of pilots and improves accuracy. Currently, accuracy analysis of UL/DL representation primarily relies on simulation. To bridge this gap, a proportion factor (PF) operator is proposed for CDL to assess accuracy. Specifically, a qualitative analysis formula is provided for accuracy and an optimal upper bound is established. Through theoretical proof, it is demonstrated that the accuracy of CDL for representation is mainly influenced by the cross‐correlation between the pilot matrix and the dictionary matrix. Inspired by PF operator, an optimal couple dictionary learning (OCDL) algorithm using singular value decomposition (SVD) is introduced to obtain dictionary updating, aiming at high‐precision representation. By establishing normalized mean squared error (NMSE), successful representation ratio, bit error rate (BER), and constellation performance, an OCDL algorithm that outperforms existing methods is showcased and channel representation accuracy is enhanced significantly.

A rapid fingerprint positioning method based on deep convolutional neural network for MIMO-OFDM systems

The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.

Multi-Terminal DC Transformer for Renewable Energy Cluster Grid Connection

An AC (alternative current) power integration of distributed energies faces multi-fold challenges such as synchronization and weak grid-induced instability. In this study, a multi-terminal DC transformer is proposed for renewable energy clusters grid connection. The DC transformer provides multiple DC input ports for renewable energy collection, while the load port is connected to the medium-voltage DC grid via a modular multilevel converter (MMC). The multi-port topology enables flexible power transfer between multiple input sources to the load without additional components. The carrier layer modulation strategy is implemented to balance the MMC module voltage; bidirectional power transmission between multiple input sources is achieved through the phase shift modulation (PSM) method. First, we provided a detailed introduction of the proposed topology and working principle. A simulation model was built using the SIMULINK, and the simulation results verified the effectiveness of the proposed converter modulation strategy and phase shift modulation method. A corresponding hardware experimental platform was designed and built, and the modulation and voltage equalization functions of modular multilevel rectifiers were presented as well as the measured results of power transmission modulation of the converter under single-input and multi-input conditions. The results indicate that the proposed transformer can achieve multiple DC inputs and has multi-channel power transmission capabilities, making it suitable for renewable energy clusters grid connection.

A synthesis of machine learning and internet of things in developing autonomous fleets of heterogeneous unmanned aerial vehicles for enhancing the regenerative farming cycle

The use of Unmanned Aerial Vehicles (UAVs) for agricultural monitoring and management offers additional advantages over traditional methods, ranging from cost reduction to environmental protection, especially when they utilize Machine Learning (ML) methods, and Internet of Things (IoT). This article presents an autonomous fleet of heterogeneous UAVs for use in regenerative farming the result of a synthesis of Deep Reinforcement Learning (DRL), Ant Colony Optimization (ACO) and IoT. The resulting aerial framework uses DRL for fleet autonomy and ACO for fleet synchronization and task scheduling inflight. A 5G Multiple Input Multiple Output-Long Range (MIMO-LoRa) antenna enhances data rate transmission and link reliability. The aerial framework, which has been originally prototyped as a simulation to test the concept, is now developed into a functional proof-of-concept of autonomous fleets of heterogeneous UAVs. For assessing performance, the paper uses Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI). The 5G MIMO-LoRa antenna produces improved results with four key performance indicators: Reflection Coefficient (S11), Cumulative Distribution Functions (CDF), Power Spectral Density Ratio (Eb/No), and Bit Error Rate (BER).

Sound and vibration control crocodile hatching

Abstract The birth of a baby or the hatching of an egg are moments when the risk of predation is particularly high. In oviparous species where clutches contain many eggs, one strategy for limiting predation is based on mechanisms that synchronise hatching, which reduces the individual risk of being targeted and may allow hatchlings to emerge under parental protection. These mechanisms involve communication signals between eggs and between eggs and parents. While previous studies in various species have shown the importance of sound and vibration signals in these exchanges, how embryos integrate this information remains poorly understood. Here we show that crocodile embryos respond differently to calls emitted by siblings (hatching calls) and to vibrations mimicking parental intervention (rubbing and scratching the shell) or the presence of a predator, suggesting that these two stimuli carry different information. Through playback experiments, we confirm that hatching calls elicit vocal responses and synchronous hatching in mature embryos, while embryos hatch earlier when they perceive vibrations. Our study underlines that the control of hatching by external factors can rely on differential apprehension of multiple sensory inputs by embryos.

Modified hybrid fruit fly-based salp swarm optimization strategy for energy efficient optimization in MIMO Wireless Powered Communication Networks

A potential way to handle the future requirements of wireless data traffic is the Massive Multiple Input Multiple Output (MIMO) antenna systems. The most effective method to satisfy the demand for wireless data traffic is to enhance the Spectral Efficiency of the existing spectrum since the wireless spectrum is a limited resource. In the MIMO network, cell-free, energy-efficient, and user-centric are considered as most important parameters to achieve effective communication. Therefore, a new energy efficiency optimization scheme is developed in a massive MIMO system to improve the system’s capacity and spectral efficiency. The multi-channel optimization problem is effectively rectified with the help of this newly designed energy efficiency optimization scheme. Here, the “Singular Value Decomposition (SVD)” method is utilized for the implementation of a sub-channel grouping scheme, where the sub-channels are arranged in descending order based on the results attained from SVD. After arranging the sub-channels, the sub-channel grouping is carried out, and then the energy efficiency optimization is provided with the help of Integrated Fruit Fly with Salp Swarm Optimization (IFFSSO). This energy-efficient algorithm improves the system capacity and spectral efficiency. The experimental outcome is revealed through various conventional models to ensure the energy efficiency of the recommended model.

Data Envelopment Analysis and Models in Healthcare Services: A Review

Data Envelopment Analysis (DEA) is a widely used method for measuring efficiency and performance in healthcare services. In this article, we will examine the role of DEA and the models used in the healthcare sector. DEA is a method used to measure the efficiency of units (such as hospitals, clinics) with multiple inputs and outputs. Essentially, data envelopment analysis conducts a performance analysis that shows how units utilize their existing resources and how they can optimize these resources. Units such as hospitals, clinics, and healthcare organizations must deliver more services with limited resources. DEA helps these organizations to use their resources most efficiently. DEA is used to increase operational efficiency, reduce costs, and improve service quality in healthcare services. There are various models available for data envelopment analysis. The most common ones include Data Envelopment Analysis (DEA) and Stochastic Data Envelopment Analysis (SDEA). While DEA is used for measuring efficiency that can be either constant or variable in scale, SDEA considers uncertainties and random effects. There are various advantages and disadvantages. Facilitates efficient use of resources, brings a data-driven approach to the decision-making process and objectively evaluates performance in healthcare services. Disadvantages are data deficiencies or poor quality may pose challenges in some cases and the complexity of the model may complicate the application and interpretation process. Data envelopment analysis in healthcare services is a powerful tool for measuring and improving the efficiency of organizations. However, proper use of data and careful application of the model are essential.

Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors

Recently, there has been a huge increase in the different ways to manufacture polymer-based sensors. Methods like additive manufacturing, microfluidic preparation, and brush painting are just a few examples of new approaches designed to improve sensor features like self-healing, higher sensitivity, reduced drift over time, and lower hysteresis. That being said, we believe there is still a lot of potential to boost the performance of current sensors by applying modeling, classification, and machine learning techniques. With this approach, final sensor users may benefit from inexpensive computational methods instead of dealing with the already mentioned manufacturing routes. In this study, a total of 96 specimens of two commercial brands of Force Sensing Resistors (FSRs) were characterized under the error metrics of drift and hysteresis; the characterization was performed at multiple input voltages in a tailored test bench. It was found that the output voltage at null force (Vo_null) of a given specimen is inversely correlated with its drift error, and, consequently, it is possible to predict the sensor’s performance by performing inexpensive electrical measurements on the sensor before deploying it to the final application. Hysteresis error was also studied in regard to Vo_null readings; nonetheless, a relationship between Vo_null and hysteresis was not found. However, a classification rule base on k-means clustering method was implemented; the clustering allowed us to distinguish in advance between sensors with high and low hysteresis by relying solely on Vo_null readings; the method was successfully implemented on Peratech SP200 sensors, but it could be applied to Interlink FSR402 sensors. With the aim of providing a comprehensive insight of the experimental data, the theoretical foundations of FSRs are also presented and correlated with the introduced modeling/classification techniques.

Striatal lateral inhibition regulates action selection in a mouse model of levodopa-induced dyskinesia.

Striatal medium spiny neurons (MSNs) integrate multiple external inputs to shape motor output. In addition, MSNs form local inhibitory synaptic connections with one another. The function of striatal lateral inhibition is unknown, but one possibility is in selecting an intended action while suppressing alternatives. Action selection is disrupted in several movement disorders, including levodopa-induced dyskinesia (LID), a complication of Parkinson's disease (PD) therapy characterized by involuntary movements. Here, we identify chronic changes in the strength of striatal lateral inhibitory synapses in a mouse model of PD/LID. These synapses are also modulated by acute dopamine signaling. Chemogenetic suppression of lateral inhibition originating from dopamine D2 receptor-expressing MSNs lowers the threshold to develop involuntary movements in vivo , supporting a role in motor control. By examining the role of lateral inhibition in basal ganglia function and dysfunction, we expand the framework surrounding the role of striatal microcircuitry in action selection.

Role of delay in brain dynamics

Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and M multiple delays between successive layers, thereby generating a polynomial time-series outputs with M. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with M outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.

Nonideal mixing effect on the input multiplicity and controller performance of CSTR based on Cholette’s model – a simulation study

ABSTRACT The transfer function of the lead-lag process in a CSTR based on Cholette’s model is used to facilitate the input multiplicity analysis. Ideal and nonideal mixing are introduced to investigate the mixing condition effect on the CSTR dynamic behavior. It is verified that the input multiplicity origins include the output multiplicity and the nonideal mixing bypass effect. The former leads to instability in the first-order process, while the latter transforms the first-order process into a lead-lag process and introduces a positive zero transfer function. The start-up diagrams established with both ideal and nonideal mixings are of critical importance to identify the unique and multiple operating regions. We also analyzed the stability conditions based on the pole and the zero of the lead-lag process, which is determined by the nonideal mixing and dominates the behaviors of a real isothermal CSTR with control. The linear and nonlinear integral controller regulatory response simulation results demonstrate that the controller performance could be deteriorated by nonideal mixing.

Review on Preamble-Based Channel Estimation Method for FBMC/OQAM Toward 6G: Advantages, Challenges and Future Works

Filter bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM) is a multicarrier modulation that is expected to replace orthogonal frequency division multiplexing (OFDM) in future sixth-generation (6G) networks. FBMC/OQAM has high spectrum efficiency, cyclic prefix (CP)-free transmission, decreased out-of-band emission (OOBE), and asynchronous environment robustness. However, the orthogonality criteria of FBMC/OQAM are only in the real field. Therefore, imaginary components of complex-valued OQAM symbols cause imaginary interferences among subcarriers, affecting channel estimation (CE) processing operations. Channel estimation is a critical component of wireless communication systems. Channel estimate allows the receiver to approximate channel impulse response (CIR) to determine the impacts of the communication channel on the sent symbols. So, an accurate channel estimate is critical in FBMC/OQAM. In this review, we focus on the Preamble-based method, one of the basic methods for channel estimation in FBMC. Three preamble-based approaches have been studied: the interference approximation method (IAM), the interference cancellation method (ICM), and pairs of pilots (POP) using a single antenna and multiple input multiple outputs (MIMO). Compare them regarding bit error rate (BER) and mean square error. Also, it compares different interference approximation methods in terms of bit error rate (BER), magnitude, and peak average power ratio (PAPR). The review found the superiority of M-IAM and NPS in spectrum efficiency and PAPR. Future work that can help the researcher in this field.

FPCC: Detecting Floating-Point Errors via Chain Conditions

Floating-point arithmetic is notorious for its rounding errors, which can propagate and accumulate, leading to unacceptable results. Detecting inputs that can trigger significant floating-point errors is crucial for enhancing the reliability of numerical programs. Existing methods for generating error-triggering inputs often rely on costly shadow executions that involve high-precision computations or suffer from false positives. This paper introduces chain conditions to capture the propagation and accumulation of floating-point errors, using them to guide the search for error-triggering inputs. We have implemented a tool named FPCC and evaluated it on 88 functions from the GNU Scientific Library, as well as 21 functions with multiple inputs from previous research. The experimental results demonstrate the effectiveness and efficiency of our approach: (1) FPCC achieves 100% accuracy in detecting significant errors for the reported rank-1 inputs, while 72.69% rank-1 inputs from the state-of-the-art tool ATOMU can trigger significant errors. Overall, 99.64% (1049/1053) of the inputs reported by FPCC can trigger significant errors, whereas only 19.45% (141/723) of the inputs reported by ATOMU can trigger significant errors; (2) FPCC exhibits a 2.17x speedup over ATOMU in detecting significant errors; (3) FPCC also excels in supporting functions with multiple inputs, outperforming the state-of-the-art technique. To facilitate further research in the community, we have made FPCC available on GitHub at https://github.com/DataReportRe/FPCC.

Influence of Multiple Repair Welding on Microstructure and Properties of 06Cr19Ni10 Stainless Steel

AbstractRepair welding technology is widely used in the manufacturing and maintenance of rail transit equipment to repair welding defects. However, repair welding induces modifications in joint performance, and it is necessary to study the microstructure evolution behavior to reveal the reasons. In this study, the effects of multiple repair welding on the microstructure, mechanical, and fatigue properties of 06Cr19Ni10 stainless steel samples were studied. The surface texture and fracture morphology analyses of the samples were conducted by optical microscopy (OM), scanning electron microscopy (SEM), and its equipped backscattered electron diffraction (EBSD) technique. The mechanical and fatigue properties of the samples with different repair welding times were further obtained by hardness, tensile, and fatigue tests. The results show an increase in the grain size and the substructure content in the heat-affected zone (HAZ), and the austenite orientation is changed, attributable to multiple repair welding. Multiple heat inputs result in a significant increase in hardness from 165 HV to 185 HV, a noticeable decrease in tensile strength and elongation, and an upward trend in yield strength. Under the constant stress level, the heat input of multiple repair welding causes a decrease in the fatigue life and significantly reduces toughness in the instantaneous fracture zone of the secondary repair sample.

Design of a compact quad-port wideband printed MIMO antenna for vehicular communication environment

ABSTRACT A wideband quad-port Multiple Input Multiple Output (MIMO) antenna with efficient isolation characteristics for 5 G New Radio (5 G-NR) applications is presented. The proposed antenna works in the span of 3–6 GHz frequency covering the n77/n78/n79 bands also covering the 5 G-V2X band (3300–5000 MHz), LTE-46 band (5150–5925 MHz) and Dedicated Short Range Communication (DSRC) at 5.9 GHz band. The single element of the MIMO antenna structure is modelled by an elliptical structure stacked on a rectangular patch along with the feedline on the conducting plane. The ground plane is modelled as a rhombic design providing a Defected Ground Structure (DGS) to provide the resonance at the desired frequency band. Later, the antenna is evolved as a four-element MIMO system by arranging the antenna elements orthogonal to each other with a distance of separation between the adjacent antenna elements considered to be 10 mm. The all-inclusive dimension of the proposed antenna is 65 × 65 mm2. The simulated MIMO structure is fabricated and measured for antenna parameters and MIMO parameters such as isolation characteristics and MIMO diversity performances. The simulated values are obtained at acceptable limits with the measured values for the proposed wideband frequency range of operation. The compact, wideband antenna is found to be suitable for various 5 G-NR applications, especially Wi-Fi-assisted Vehicular Communication in the sub-6 GHz frequency range.