Input Path Research Articles

Adaptive Control With Guaranteed Transient Behavior and Zero Steady-State Error for Systems With Time-Varying Parameters

It is nontrivial to achieve global zero-error regulation for uncertain nonlinear systems. The underlying problem becomes even more challenging if mismatched uncertainties and unknown time-varying control gain are involved, yet certain performance specifications are also pursued. In this work, we present an adaptive control method, which, without the persistent excitation (PE) condition, is able to ensure global zero-error regulation with guaranteed output performance for parametric strict-feedback systems involving fast time-varying parameters in the feedback path and input path. The development of our control scheme benefits from generalized <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -dependent and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> -dependent functions, a novel coordinate transformation and “congelation of variables” method. Both theoretical analysis and numerical simulation verify the effectiveness and benefits of the proposed method.

Numerical Analysis of Laser Pattern Effects to Residual Stress on Metal 3D Printing

In this study, a metal 3D printing process was simulated using finite elements methods (FEM), and the specimens were printed under the same conditions. Subsequently, residual stress was measured to validate the results. The thermal-structure two-way coupled analysis confirmed the phenomenon that occurred during the additive process, thereby allowing the residual stress to be calculated more realistically. In addition, to simulate the printing process, a subroutine was configured to account for the laser heat input path and layer. The process of stacking and hatching in a snake pattern for an area measuring 5 mm × 5 mm was simulated. Four cases with different rotation angles of the layer pattern were calculated using FEM. The specimens were printed compared with the analysis results. To verify the printed condition of the specimen, computed tomography was performed to confirm the appearance of pores and cracks in the specimen. Cracks appeared in the 180° specimen, and the cause was analyzed based on the analysis results. Subsequently, the residual stress was measured by an X-ray diffractometer and compared; it was confirmed that the average error of the specimen without cracks is 8.86%, which is similar to the analysis results. These results confirm that the FEM model conducted in this study can be used to analyze residual stress and cracks in a material, which are difficult to analyze in previous studies. The FEM model constructed in this study is expected to facilitate investigations into 3D printing phenomena as well as enable a more efficient process design.

Алгоритм обробки сигналів для вертолітного широкосмугового шумового некогерентного радіовисотоміру

The algorithm for a helicopter or an unmanned aerial vehicle flight altitude measuring based on the processing of broadband and ultra-wideband pulsed stochastic signals has been synthesized for the first time by the maximum likelihood method. When formulating the initial data, mathematical models of the probing signal and the received observation are given, taking into account both various options for input path implementation, which impose restrictions on the observation equation form, and the geometry of the problem. When solving the problem, the statistical characteristics of the given models were found and studied. The calculated observation correlation function contains information about both the signal delay time and the radio pulse envelope, which makes it possible to obtain the algorithm for desired altitude parameter determining by one of two ways: differentiating the likelihood functional by the delay time or by the radio pulse envelope. At the same time, for the first time, the inversion equation for the statistical characteristics of the studied non-stationary processes in the frequency domain has been obtained. Such processes arise due to the presence of a radio pulse envelope. An important feature of the solved synthesis problem is a noise pulse transmitter use that implements the function of the underlying surface sounding, as well as taking into account the fact of the signal structure destruction during its radiation, propagation and reflection. Such a destruction of the signal shape doesn’t make it impossible to synthesize a radar with internal coherent processing algorithms when working on one receiving antenna and requires the search for other signal processing options. The use of a non-deterministic signal in the system also complicates the formalization of the delay time parameter in the likelihood function, since in this case the reference signal cannot be represented as a model or an analytical equation. Following the synthesized algorithm, a simulation model of a pulsed radar with a stochastic probing signal has developed and the results of its modeling are presented. The obtained output effect of the system fully corresponds to the classical theoretical calculations.

Improved MRAS Rotor Position Observer Based on Control Winding Power Factor for Stand-Alone Brushless Doubly-Fed Induction Generators

The main contribution of this study is to propose a new rotor position observer with the approach of model reference adaptive system (MRAS) associated with the quantity of control winding (CW) power factor of the brushless doubly-fed induction generator (BDFIG) for sensorless voltage control target in stand-alone systems. The proposed observer relies on the field-orientation scheme of power winding (PW) side. The proposed sensorless methodology does not need any computation process with integration for obtaining the flux components. In addition, the new methodology can reduce the voltage sensors required for detecting the actual CW voltage quantities to be used in the calculations process of the proposed MRAS procedure by directly realizing the CW reference stationary voltages from the input path of SVPWM. This would ensure the simplicity of the suggested procedure for detecting the rotational position of the generator. The stability confirmation of the presented estimation method is analyzed and realized with the aid of technique of hyperstability concept and aided with the criteria of Popov's analysis. Moreover, the verification of robustness for the proposed observer is studied via uncertainty issues of machine parameters. Comprehensive simulation and experimental results are carried out to verify this method,

A New Study on Optimization of Four-Bar Mechanisms Based on a Hybrid-Combined Differential Evolution and Jaya Algorithm

In mechanism design with symmetrical or asymmetrical motions, obtaining high precision of the input path given by working requirements of mechanisms can be a challenge for dimensional optimization. This study proposed a novel hybrid-combined differential evolution (DE) and Jaya algorithm for the dimensional synthesis of four-bar mechanisms with symmetrical motions, called HCDJ. The suggested algorithm uses modified initialization, a hybrid-combined mutation between the classical DE and Jaya algorithm, and the elitist selection. The modified initialization allows generating initial individuals, which are satisfied with Grashof’s condition and consequential constraints. In the hybrid-combined mutation, three differential groups of mutations are combined. DE/best/1 and DE/best/2, DE/current to best/1 and Jaya operator, and DE/rand/1, and DE/rand/2 belong to the first, second, and third groups, respectively. In the second group, DE/current to best/1 is hybrid with the Jaya operator. Additionally, the elitist selection is also applied in HCDJ to find the best solutions for the next generation. To validate the feasibility of HCDJ, the numerical examples of the symmetrical motion of four-bar mechanisms are investigated. From the results, the proposed algorithm can provide accurate optimal solutions that are better than the original DE and Jaya methods, and its solutions are even better than those of many other algorithms that are available in the literature.

An 8-Gb GDDR6X DRAM Achieving 22 Gb/s/pin With Single-Ended PAM-4 Signaling

Demand for dynamic random access memory (DRAM) bandwidth has outpaced DRAM transistor performance. Given the options of major process investment to scale beyond sixth-generation graphics double-data-rate (GDDR6) or replace GDDR6 with costly high bandwidth memory (HBM), this article presents a solution that simultaneously increases pin and energy efficiency through the integration of four-level pulse amplitude modulation (PAM-4) into the single-ended memory interface. Building upon the existing GDDR6 architecture, evolutionary modifications to input, output, clocking, and data path, along with the component package design, enable a per-pin data rate of more than 22 Gb/s.

Robust state-of-charge estimation of Li-ion batteries based on multichannel convolutional and bidirectional recurrent neural networks

Due to the lack of multiscale feature extraction and bidirectional feature learning abilities, the existing deep state-of-charge (SOC) estimators are difficult to capture: (1) localized invariant characteristics hidden in battery measurement perturbations at multiple scales; and (2) intercorrelations among measurements in both time and reverse-time orders. If these situations are not considered, it will lead to large fluctuations in estimated SOC and accumulation of estimated errors at continuous time steps To solve these problems, an estimator combining multichannel convolutional and bidirectional recurrent neural networks (MCNN-BRNN) is proposed for SOC estimation. Specifically, MCNN can extract multiscale local robust features that are invariant to perturbations from measurements on different input paths reducing the estimation fluctuations and enhancing the robustness of the estimator. Moreover, a global convolutional layer is designed to learn the intercorrelations of multiscale features and preserve their temporal coherence. By this means, BRNN can capture the effective time-varying information of intercorrelated features in the forward and reverse directions to sequentially estimate SOC, thus alleviating the error accumulation and improving the overall estimation accuracy. Experiments results reveal that MCNN-BRNN outperforms the state-of-the-art estimators in terms of robustness and accuracy under the situations where multiscale perturbations and their comovements exist in measurements.

SIMULATION OF THE P-I-N-STRUCTURES CHARACTERISTICS INTERACTING WITH HIGH-FREQUENCY ELECTROMAGNETIC RADIATION BY PERTURBATION THEORY METHODS

The mathematical model is proposed for the electron-hole plasma characteristics predicting of the active region (i-region) of p-i-n structures, taking into account the effect of the displacement current (induced by an external microwave electromagnetic field) on the formation of a space charge. The model is based on a singularly perturbed nonlinear boundary value problem for the system of the electron-hole current continuity equations and Poisson’s (the Poisson’s equation contains a naturally formed small parameter). The model nonlinear boundary value problem is reduced to a recurrent sequence of linear boundary value problems for determining of the electron-hole plasma concentrations and the electric potential distributions in the i-region by using asymptotic methods, in particular, the boundary layer method and complex amplitudes. The proposed mathematical model feature is that it reflects the role of the boundary layer in the p-i-n structure contact zones in the formation of a space charge and takes into account the effect of a displacement current caused by microwave radiation. An algorithm for predicting the charge carrier concentrations distribution in the electron-hole plasma is constructed. It is shown that an extraneous microwave field entails an additional increase in the stationary concentration of electrons and holes in the active region, against which the plasma concentration fluctuates at the microwave field frequency (the appearance of higher harmonics is not taken into account in the model). The effect of the growth of the concentration stationary component is more pronounced in the zones of p-i-, n-i-contacts. The proposed mathematical model and the developed algorithm for analyzing the problem posed are important for developers of microwave electronics semiconductor elements, in particular, p-i-n structures used for switching powerful electromagnetic fields and as protective devices for the input paths of radio engineering systems.

Chronic, Mild Vestibulopathy Leads to Deficits in Spatial Tasks that Rely on Vestibular Input While Leaving Other Cognitive Functions and Brain Volumes Intact.

Objectives: In this study, based on the known vestibulo-hippocampal connections, we asked whether mild chronic vestibulopathy leads only to vestibular-related deficits or whether there are effects on hippocampal function, structure, and cognition in general. In more detail, we assessed whether chronic vestibulopathy leads to (a) deficits in vestibular tasks without cognitive demand (balancing), (b) deficits in spatial cognitive tasks that require vestibular input (path integration, rotational memory), (c) deficits in spatial cognitive tasks that do not rely on vestibular input, (d) deficits in general cognitive function, and (e) atrophy in the brain. Methods: A total of 15 patients with chronic uni- or bilateral vestibulopathy (56.8 ± 10.1 years; 4 females) were included in this study and were age- and gender-matched by the control participants (57.6 ± 10.5) in a pairwise manner. Given their clinical symptoms and their deficits of the vestibulo-ocular reflex (VOR) the patients could be classified as being mildly affected. All participants of the underwent the following tests: clinical balance (CBT), triangle completion (TCT) for path integration, rotational memory (RM), the visuo-spatial subset of the Berlin intelligence structure test (BIS-4) and d2-R for attention and concentration, and a structural MRI for gray matter analysis using voxel-based morphometry (VBM). Results: Compared to the healthy controls, the vestibulopathy patients performed significantly worse in terms of CBT, TCT, and RM but showed no differences in terms of the BIS-4 and d2-R. There were also no significant volumetric gray matter differences between the two groups. Conclusions: This study provides evidence that both non-cognitive and cognitive functions that rely on vestibular input (balancing, path integration, rotational memory) are impaired, even in mild chronic vestibulopathy, while other cognitive functions, which rely on visual input (visuo-spatial memory, attention), are unimpaired in this condition, together with an overall intact brain structure. These findings may reflect a segregation between vestibular- and visual-dependent processes in the medial temporal lobe on the one hand and a structure–function dissociation on the other.

Differences in organic matter properties and microbial activity between bulk and rhizosphere soil from the top- and subsoils of three forest stands

The distribution of soil organic carbon (SOC) in subsoil is much more heterogeneous than in topsoils. This is largely caused by more localized inputs of fresh substrates and nutrients that mainly derive from rhizodeposition creating hotspots of microbial activity.To elucidate the role of rhizosphere soil as an important input path for substrates into deeper soil layers, we investigated differences of organic matter distribution and microbial degradation in the rhizosphere soil and the surrounding bulk topsoil as well as in the subsoil. In addition, we considered the effect of different parent materials on SOC dynamics by analyzing soils developed in pleistocene sand, triassic sandstone and loess all covered with similarly aged beech forest within the same climatic region. Besides basic soil parameters, basal respiration, microbial biomass (Cmic) and 14C content were determined. Further, extracellular enzymes involved in C-, N- and P-cycling were analyzed. Differences in the microbial community were investigated using a taxon-specific real time qPCR approach. The results revealed significant differences of the microbial community composition between bulk and rhizosphere in top- and subsoil at the investigated sample sites. The mean SOC contents were 322% and 475% higher in the rhizosphere soil in both the top- and subsoil, respectively. The microbial biomass (Cmic) in the rhizosphere was up to 80% higher in the topsoil and on average 200% higher in the subsoil (SOC normalized), compared to the respective bulk soil. The qPCR results for all samples showed a higher diversity and abundance in the rhizosphere compared to the respective bulk samples and a shift in community composition with increasing soil depth. Total activity of the analyzed extracellular enzymes involved in different nutrient cycles differed significantly between bulk and rhizosphere soil and revealed that the microbial community in the rhizosphere subsoil can effectively utilize substrates even under scarce availability. The SOC normalized basal respiration at the Pleistocene sand and Loess sites and K2SO4-extractable C were higher in the subsoil compared to the topsoil showing more labile C in deeper soil layers with younger 14C in the rhizosphere. These results show that the rhizosphere at the investigated forest sites is a hotspot for microbial activity, which is more pronounced in the subsoil where the relevance of these hotspots is important for C turnover.

Radar imaging complex with SAR and ASR for aerospace vechicle

The subject of study in the article is the algorithms for radio monitoring of the Earth in a wide field of view from aerospace transport. The goal is to design a structural diagram of a radio complex that can operate simultaneously in two modes: modified synthetic aperture (SAR) and aperture synthesis (ASR), in accordance with algorithms synthesized by the maximum likelihood method. The modified SAR mode allows obtaining high-resolution radio images in the observation angle range ±(20°...50°) from the direction to the nadir. A method of combining a modified SAR algorithm is used, which differs from the classical imaging algorithm by the possibility of obtaining a higher spatial resolution, the payment for this is the complication of the signal processing algorithm associated with the implementation of decorrelating filters that expand the spectrum of received signals in each receiving path, and the ASR mode, which allows imaging using passive or active radar principles. The passive ASR mode provides for the imaging in the observation angle range of ±20° from the nadir based on the results of processing signals of its own broadband radio-thermal radiation, and the active mode – in the same observation angle range, but using the broadband noise signal of the backlight. An important result in the formation of a radio image in the specified viewing area when using the active mode of the ASR is that the images are close in physical content, namely, proportional to the specific effective reflection surface of the underlying surface. In addition, a distinctive feature of the synthesized algorithms is the use of wideband probing signals and, accordingly, the same input paths of receivers, which makes it possible to increase the signal-to-noise ratio of the output effect. Conclusions. The scientific novelty of the results obtained is as follows: a structural diagram of the radio complex was developed on the basis of algorithms synthesized by the maximum likelihood method. For the formation of a radio image in the radio complex, a combination of SAR and ASR (with two modes of operation) is implemented. This implementation is important, since it allows obtaining high-resolution images in the observation angle range of ±50° from the direction to the nadir. It is advisable to place the complex on airplanes, helicopters and spacecraft (preferably those that move in low orbits).

Multimedia fate model and risk assessment of typical antibiotics in the integrated demonstration zone of the Yangtze River Delta, China

Due to the widespread consumption of antibiotics by humans and animals, antibiotic residues from human and animal excrements are released into the environment through domestic sewage and breeding wastewater, which ultimately affect the ecological environment and human health. In this study, the concentrations of 10 antibiotics in the air, water, soil, and sediment from 2013 to 2019 in Qingpu District of the integrated demonstration zone of the Yangtze River Delta were predicated by developing a dynamic Level IV fugacity model. The influence of seasonal environmental factors (e.g., temperature, rainfall) on the distribution and migration of antibiotics in multi-media was also explored. The simulation results show that the 10 antibiotics mainly existed in water and sediment. The concentrations of antibiotics in air, water, soil, and sediment were 0–7.629 × 10−14 ng/L, 1.187 × 10−10-16.793 ng/L, 1.042 × 10−14-3.500 × 10−11 ng/g and 8.015 × 10−12-14.188 ng/g, respectively. It was also found that the increase in temperature and rainfall can reduce the migration rate of some antibiotics into the water and sediment phases. The flux analysis of the cross-media migration and transformation of antibiotics in Qingpu District shows that advection was the prime input and output paths of antibiotics in the water. Moreover, the prime input and output paths of antibiotics in sediment were sedimentation from water to sediment and degradation. Sensitivity analysis shows that the characteristics of antibiotic emission, degradation rate, and Koc were the most influential parameters for target chemicals. The results of risk assessment based on Monte Carlo method reveal that the overall risk level of antibiotics in sediment was relatively risk-free, and the risk of antibiotics in water decreased in the order of tetracyclines > β-lactams > fluoroquinolones > macrolides > sulfonamides.

GCAUNet: A group cross-channel attention residual UNet for slice based brain tumor segmentation

Precise brain tumor segmentation can improve patient prognosis. However, due to the complicated structure of the human brain, brain tumor segmentation is a challenging task. To improve the brain tumor segmentation performance, a group cross-channel attention residual UNet (GCAUNet) that can make full use of the low-level fine details of tumor regions is proposed. First, iterative background removal and image normalization are applied to remove the disturbances of the background and illumination variations, respectively. Then, GCAUNet is constructed for brain tumor segmentation. To recover the fine details of brain tumors, a parallel network path, namely, the detail recovering (DR) path, is introduced to extract detail feature groups from multiscale low-level feature maps. In addition, to emphasize the significant feature groups and channels, a coarse-to-fine cross channel attention module, namely, the group cross-channel attention (GCA) module, is proposed. Furthermore, a multiscale input (MI) path is introduced into GCAUNet to acquire multiscale context information. The experimental results show that the average dices of GCAUNet for the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) on BraTS 2017 and BraTS 2018 reach 87.2%/79.6%/78.1% and 90.9%/84.5%/81.3%, respectively. Compared with the backbone, the dices of WT, TC and ET on BraTS 2017 and 2018 are improved by 4.8%/7.1%/6.2% and 4.1%/3.5%/3.1%, respectively, which indicates that GCAUNet can significantly improve the brain tumor segmentation performance.

Lost gamma source detection algorithm based on convolutional neural network

Based on the convolutional neural network (CNN), a novel technique is investigated for lost gamma source detection in a room. The CNN is trained with the result of a GEANT4 simulation containing a gamma source inside a meshed room. The dataset for the training process is the deposited energy in the meshes of different n-step paths. The neural network is optimized with parameters such as the number of input data and path length. Based on the proposed method, the place of the gamma source can be recognized with reasonable accuracy without human intervention. The results show that only by 5 measurements of the energy deposited in a 5-step path, (5 sequential points 50 cm apart within 1600 meshes), the gamma source location can be estimated with 94% accuracy. Also, the method is tested for the room geometry containing the interior walls. The results show 90% accuracy with the energy deposition measurement in the meshes of a 5-step path.

Numerosities and Other Magnitudes in the Brains: A Comparative View.

The ability to represent, discriminate, and perform arithmetic operations on discrete quantities (numerosities) has been documented in a variety of species of different taxonomic groups, both vertebrates and invertebrates. We do not know, however, to what extent similarity in behavioral data corresponds to basic similarity in underlying neural mechanisms. Here, we review evidence for magnitude representation, both discrete (countable) and continuous, following the sensory input path from primary sensory systems to associative pallial territories in the vertebrate brains. We also speculate on possible underlying mechanisms in invertebrate brains and on the role played by modeling with artificial neural networks. This may provide a general overview on the nervous system involvement in approximating quantity in different animal species, and a general theoretical framework to future comparative studies on the neurobiology of number cognition.

Two-Way MR-Forest Based Growing Path Classification for Malignancy Estimation of Pulmonary Nodules.

This paper proposes a two-way multi-ringed forest (TMR-Forest) to estimating the malignancy of the pulmonary nodules for false positive reduction (FPR). Based on our previous work of deep decision framework, named MR-Forest, we generate a growing path mode on predefined pseudo-timeline of L time slots to build pseudo-spatiotemporal features. It synchronously works with FPR based on MR-Forest to help predict the labels from a dynamic perspective. Concretely, Mask R-CNN is first used to recommend the bounding boxes of ROIs and classify their pathological features. Afterward, hierarchical attribute matching is introduced to obtain the input ROIs' attribute layouts and select the candidates for their growing path generation. The selected ROIs can replace the fixed-sized ROIs' fitting results at different time slots for data augmentation. A two-stage counterfactual path elimination is used to screen out the input paths of the cascade forest. Finally, a simple label selection strategy is executed to output the predicted label to point out the input nodule's malignancy. On 1034 scans of the merged dataset, the framework can report more accurate malignancy labels to achieve a better CPM score of 0.912, which exceeds those of MR-Forest and 3DDCNNs about 2.8% and 4.7%, respectively.

Adaptive Control for Systems With Time-Varying Parameters

This article investigates the adaptive control problem for systems with time-varying parameters using the so-called congelation of variables method. First, two scalar examples to illustrate how to deal with time-varying parameters in the feedback path and in the input path, respectively, are discussed. The control problem for an n-dimensional lower triangular system via state feedback is then discussed to show how to combine the congelation of variables method with adaptive backstepping techniques. To achieve output regulation problem via output feedback, problem which cannot be solved directly due to the coupling between the input and the time-varying perturbation, the ISS of the inverse dynamics, referred to as strong minimum-phaseness, is exploited. This allows converting such coupling into the coupling between the output and the time-varying perturbation. A set of filters, resulting in ISS state estimation error dynamics, are designed to cope with the unmeasured state variables. Finally, a controller is designed based on a small-gain-like analysis that takes all subsystems into account. Simulation results show that the proposed controller achieves asymptotic output regulation and outperforms the classical adaptive controller, in the presence of time-varying parameters that are neither known nor asymptotically constant.

Horizontal Hippocampal Slices of the Mouse Brain.

The hippocampus is a highly organized structure in the brain that is a part of the limbic system and is involved in memory formation and consolidation as well as the manifestation of severe brain disorders, including Alzheimer's disease and epilepsy. The hippocampus receives a high degree of intra- and inter-connectivity, securing a proper communication with internal and external brain structures. This connectivity is accomplished via different informational flows in the form of fiber pathways. Brain slices are a frequently used methodology when exploring neurophysiological functions of the hippocampus. Hippocampal brain slices can be used for several different applications, including electrophysiological recordings, light microscopic measurements as well as several molecular biological and histochemical techniques. Therefore, brain slices represent an ideal model system to assess protein functions, to investigate pathophysiological processes involved in neurological disorders as well as for drug discovery purposes. There exist several different ways of slice preparations. Brain slice preparations with a vibratome allow a better preservation of the tissue structure and guarantee a sufficient oxygen supply during slicing, which present advantages over the traditional use of a tissue chopper. Moreover, different cutting planes can be applied for vibratome brain slice preparations. Here, a detailed protocol for a successful preparation of vibratome-cut horizontal hippocampal slices of mouse brains is provided. In contrast to other slice preparations, horizontal slicing allows to keep the fibers of the hippocampal input path (perforant path) in a fully intact state within a slice, which facilitates the investigation of entorhinal-hippocampal interactions. Here, we provide a thorough protocol for the dissection, extraction, and acute horizontal slicing of the murine brain, and discuss challenges and potential pitfalls of this technique. Finally, we will show some examples for the use of brain slices in further applications.

Low Power Structure Digital Up Converter Design for Software Radio System

The numerically controlled oscillator is often supposed to operate after interpolation in a conventional digital up converter, but such a converter works at quite high frequencies and consumes more power. In this paper, a type of low power structure digital up converter is proposed which carries out mixing and interpolating gradually, where the numerically controlled oscillator and the interpolation filter can both work at a lower frequency. Low power structure can move signal to any position within the whole Nyquist Band. Furthermore, the interpolation filter adopts a half-band filter with the structure of two parallel branches, which combines the functions of stuffing zero, filtering and modulation, and makes programmable modulation by exchanging input paths. A simulation is carried out on the Xilinx KC705 development kit, which indicates that its maximum work frequency of the low power structure reaches 392[Formula: see text]MHz while its power consumption reduces by 33.3% compared to the conventional structure.

N-Way Planar Balanced-to-Balanced Series Feeding Network for Balanced Frequency-Scanning Phased Array

In this article, a novel series feeding network as an N-way balanced-to-balanced structure for balanced frequency-scanning phased array is proposed. Design requirements of the N-way balanced-to-balanced series feeding (BBSF) network are extracted and mixed-mode S-parameters are expressed by using matrix transformation. Designing the BBSF network without using baluns at input and output paths of the network results in the compact and low-loss feeding network, while maintaining the common-mode suppression feature. Furthermore, to have a planar BBSF network, microstrip crossovers are utilized to avoid the use of multilayer PCB design, resulting in the elegant transfer of intersecting RF signals. To validate the proposed structure, a three-way BBSF network is fabricated which shows acceptable mixed-mode measurement results. Also, to demonstrate the performance of the proposed structure, a balanced frequency-scanning phased array is designed employing the proposed feeding network. The measurement results of the fabricated array show that the differential return loss and the differential mode to common mode conversions are better than 20 and 30 dB at 5.85 GHz, respectively. Meanwhile, the fabricated array shows a wide-range beam-scanning capability over its frequency band.