Multiple Frequency Research Articles

EDMF: A New Benchmark for Multi-Focus Images with the Challenge of Exposure Difference

The goal of the multi-focus image fusion (MFIF) task is to merge images with different focus areas into a single clear image. In real world scenarios, in addition to varying focus attributes, there are also exposure differences between multi-source images, which is an important but often overlooked issue. To address this drawback and improve the development of the MFIF task, a new image fusion dataset is introduced called EDMF. Compared with the existing public MFIF datasets, it contains more images with exposure differences, which is more challenging and has a numerical advantage. Specifically, EDMF contains 1000 pairs of color images captured in real-world scenes, with some pairs exhibiting significant exposure difference. These images are captured using smartphones, encompassing diverse scenes and lighting conditions. Additionally, in this paper, a baseline method is also proposed, which is an improved version of memory unit-based unsupervised learning. By incorporating multiple adaptive memory units and spatial frequency information, the network is guided to focus on learning features from in-focus areas. This approach enables the network to effectively learn focus features during training, resulting in clear fused images that align with human visual perception. Experimental results demonstrate the effectiveness of the proposed method in handling exposure difference, achieving excellent fusion results in various complex scenes.

SDN-controlled multi-user NOMA-OFDM VLC system based on a resource allocation algorithm.

We propose a novel, to our knowledge, resource allocation algorithm (RAA) for a multi-user non-orthogonal multiple access orthogonal frequency division multiplexing (NOMA-OFDM) visible light communication (VLC) system. The proposed algorithm considers both the interference between users and their channel diversity. Bit allocation to users in each iteration is based on the ratio between the already allocated number of bits and the demanded number of bits of users, rather than their channel gains. In this way, the inherent issue of prioritizing strong users in a conventional RAA is avoided. We implement a software-defined-network (SDN)-controlled VLC system with real-time signal generation to validate the proposed RAA. The allocation results in the SDN platform are used to configure a FPGA-based transmitter. Experiments of a two-user NOMA-OFDM system with 115-191 Mbit/s throughput show that the system can dynamically react to the change of data demand and channel responses of users. We further demonstrate that the proposed algorithm outperforms conventional RAAs regardless of the data demand, receiving angles, and distances of users.

Design of a Multiband Antenna Using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT Applications

ABSTRACTThe rapid expansion of Internet of Things (IoT) applications presents unprecedented challenges for antenna design, demanding solutions that are versatile, efficient, and capable of operating across multiple frequency bands. This paper addresses these challenges through the development of a design of a multiband antenna using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT applications (DMA‐ACWGAN‐IoT). Here, the Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) is employed to generate synthetic data representing electromagnetic field distributions and antenna characteristics across various frequencies. The proposed metamaterial‐based Multiple‐Input Multiple‐Output (MIMO) antenna contains four discrete elements, each provided by a micro strip feed. The structures overall width and length are 60 and 52 mm. The metamaterial is printed on a patch and dispersed from the fields with the most effective coupling. The proposed structures strong impedance bandwidth works at 8.3 GHz, 10.8 GHz, 12.3 GHz, 13.7 GHz, 16.1 GHz, and 18.1 GHz, fabricating the proposed antenna prototype using a substrate made of FR‐4 material. The proposed DMA‐ACWGAN‐IoT design provides 6.5 dB maximum gain and 25.40%, 21.60%, and 20.05% higher efficiency compared to existing dual band antenna design with resonance frequency prediction under machine learning models (DBA‐PRF‐ML), machine learning verification depending on a distinctive SWB multiple slotted four‐port high isolated MIMO antenna loaded with metasurface for the applications of IoT (SWB‐MIMO‐IOT‐ML), and dual‐band miniaturized composite right–left‐handed transmission line ZOR antenna along machine learning method for microwave communication (DB‐ZORA‐MC‐ML).

Near‐Infrared Dual‐Band Frequency Comb Generation from a Silicon Resonator

AbstractBenefitting from the mature, cost‐effective, and scalable manufacturing capabilities of complementary metal‐oxide‐semiconductor (CMOS) technology, silicon photonics has facilitated the seamless and monolithic integration of diverse functionalities, including optical sources, modulators, and photodetectors. Microresonators can generate multiple coherent optical frequency comb lines and serve as optical sources. However, at the telecom band, silicon suffers from two‐photon absorption and free‐carrier absorption, which severely hampers the realization of microcombs from a single silicon chip at telecom wavelengths until now. In this paper, a novel approach is presented and demonstrated with near‐infrared dual‐band frequency combs from a multimode silicon resonator. With a single pumping configuration, dual‐band combs are generated from the interaction between the pump and Raman Stokes fields by involving two different optical mode families but with similar group velocities. It is observed that the pump power required to generate dual‐band combs is as low as 0.7 mW. The work in bringing telecom microcombs to the silicon platform will advance silicon photonics for the next generation of monolithically integrated technology based on a single silicon chip, enabling new possibilities for further exploring silicon photonics‐based applications in optical telecommunications, sensing, and quantum metrology in the telecom band using a monolithic single silicon chip.

Statement of Removal: Knowledge-based heuristic optimisation technique for mm-wave 5G antenna

ABSTRACT This work presents a knowledge-based decision-making robust antenna design (KDRAD) method that employs a heuristic-optimisation technique for antenna design. The method is used to create and scale a single-element antenna to operate at multiple frequencies within the 5 G range. In this study, a low-profile planar four-element microstrip antenna of 14.8 mm × 14.8 mm × 0.508 mm gives simulated and measured impedance bandwidths of about 1.18 GHz (26.43–27.61 GHz) and 1.08 GHz (26.42–27.50 GHz), respectively, with a centre frequency of 27 GHz. The mm-wave planar four-port antenna has a maximum gain of 7.2 dBi; isolation of 33.2 dB; and multiple antenna matrices with minimal ECC (0.000000109), high diversity gain (10 dB) and channel capacity loss of 0.00110 bits/s/Hz.

Discrete and Parallel Frequency‐Bin Entanglement Generation from Quantum Frequency Comb

AbstractPhotons’ frequency degree of freedom is promising to realize large‐scale quantum information processing. Quantum frequency combs (QFCs) generated in integrated nonlinear microresonators can produce multiple frequency modes with narrow linewidth. Here, polarization‐entangled QFCs are utilized to generate discrete frequency‐bin entangled states. Fourteen pairs of polarization‐entangled photons with different frequencies are simultaneously transformed into frequency‐bin entangled states. The characteristic of frequency‐bin entanglement is demonstrated by Hong‐Ou‐Mandel interference, which can be performed with single or multiple frequency pairs in parallel. This work paves the way for harnessing large‐scale frequency‐bin entanglement and converting between different degrees of freedom in quantum information processing.

Local and interareal alpha and low-beta band oscillation dynamics underlie the bilateral field advantage in visual working memory.

Visual working memory has a limited maximum capacity, which can be larger if stimuli are presented bilaterally vs. unilaterally. However, the neuronal mechanisms underlying this bilateral field advantage are not known. Visual working memory capacity is predicted by oscillatory delay-period activity, specifically, by a decrease in alpha (8 to 12Hz) band amplitudes in posterior brain regions reflecting attentional deployment and related shifts in excitation, as well as a concurrent increase of prefrontal oscillation amplitudes and interareal synchronization in multiple frequencies reflecting active maintenance of information. Here, we asked whether posterior alpha suppression or prefrontal oscillation enhancement explains the bilateral field advantage. We recorded brain activity with high-density electroencephalography, while subjects (n= 26, 14 males) performed a visual working memory task with uni- and bilateral visual stimuli. The bilateral field advantage was associated with early suppression of low-alpha (6 to 10Hz) and alpha-beta (10 to 17Hz) band amplitudes, and a subsequent alpha-beta amplitude increase, which, along with a concurrent load-dependent interareal synchronization in the high-alpha band (10 to 15Hz), correlated with hit rates and reaction times and thus predicted higher maximum capacities in bilateral than unilateral visual working memory. These results demonstrate that the electrophysiological basis of the bilateral field advantage in visual working memory is both in the changes in attentional deployment and enhanced interareal integration.

A high-precision two-parameter road roughness grading and identification methodology across multiple frequency intervals

Based on a dual track road shape meter, various road roughness was measured. It was discovered that the road grading method in ISO 8608 assumes a constant waviness value of W = 2 with a one-straight line approximation, but it could not accurately fit and grade the measured road roughness. A high-precision piecewise fitting function for the low-frequency and high-frequency parts of the road roughness power spectral density (PSD) is established. A high-precision two-parameter grade method in multiple frequency bands for road roughness is proposed ( G q( n0), the geometric average of the road roughness index at n0, W, the waviness, is the slope of the road roughness PSD fitting expression in the double logarithmic coordinate system). According to the vehicle driving performance obtained by simulation, the grading method is verified. For Paved and Undulating roads, compared to the road roughness classification method in ISO 8608, the performance simulated based on the grade results of the two parameters grading method are closer to the performance simulated based on the measured road roughness. The grading method proposed in this paper can more accurately fit and classify the measured road roughness. The influence of these parameters at each frequency interval on vehicle driving performance is studied. A two-parameter identification method for road roughness PSD at variable intervals over a large frequency range is proposed. In the double logarithmic coordinate system, this method divides the selected spatial frequency into three sub-intervals along the abscissa, in each sub-interval, the parameters G q( n0) and W are identified separately. This method can provide effective control inputs for the vehicle’s active or semi-active suspension system.

Formulation of Envelope Correlation Coefficient for Multiple Sensors Based on Scattering Parameter

The Envelope Correlation Coefficient (ECC) is crucial for assessing multiple sensor systems in wireless communication, sensing, and microwave imaging. ECC measures signal similarity between sensors, with higher values (close to 1.00) indicating strong correlation. Efficient power transmission, reception, and multiband path transmission enhance sensor network performance by improving data rates and reliability through multiple frequency bands. This study uses scattering parameters to calculate ECC for four different sensors in a single arrangement. Most of the sensors exhibit higher correlation at f2 and f3, while f1 shows a low ECC.

Acoustic Imaging Learning-Based Approaches for Marine Litter Detection and Classification

This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments.

Experimental and numerical investigation on the cavitation erosion effect and resonance effect of submerged water jets

ABSTRACT To analyse the cavitation erosion effect and resonance effect of submerged water jets generated by organ-pipe nozzles, experimental and numerical research is conducted in the nozzles with five different diffuser angles. The cavitation erosion intensity and the flow characteristics of submerged jets are obtained by the erosion test, high-speed photography and Large Eddy Simulation. The experimental and numerical results illustrate that the nozzle with a 60° diffuser angle is capable of producing a strong cavitation erosion effect. Compared to the fully developed submerged jet, the axial pressure oscillation of axially confined submerged jets is stronger, indicating that it has a strong resonance effect. The stress wave generated by cavitation modulates the jets after reflection from the impact wall. This modulation results in the excitation of the original main frequency oscillation of organ-pipe nozzle jets, which then gives rise to multiple frequency oscillations, thereby forming the strong resonance effect.

Visual Processing by Hierarchical and Dynamic Multiplexing.

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7-14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time-expressed as different gamma band bursts (>30 Hz)-along the alpha phase. Since the processing capacity per alpha cycle is limited, an inverse relationship between object resolution and size of attentional spotlight ensures independence of the proposed mechanism from absolute object complexity. Frequency and wave-shape of those fluctuations would depend on the nature of the object that is processed and on cognitive demands. Multiple objects would further be organized along the phase of slower fluctuations (e.g., theta), potentially driven by saccades. Complex scene processing, involving covert attention and eye movements, would therefore be associated with multiple frequency changes in the alpha and lower frequency range. This framework embraces the idea of a hierarchical organization of visual processing, independent of environmental temporal dynamics.

The topological dynamics of continuum lattice grid structures

Continuum lattice grid structures which consist of joined elastic beams subject to flexural deformations are ubiquitous. In this work, we establish a theoretical framework of the topological dynamics of continuum lattice grid structures, and discover the topological edge and corner modes in these structures. We rigorously identify the infinitely many topological edge states within the bandgaps via a theorem, with a clear criterion for the infinite number of topological phase transitions. Then, we obtain analytical expressions for the topological phases of bulk bands, and propose a topological index related to the topological phases that determines the existence of the edge states. The theoretical approach is directly applicable to a broad range of continuum lattice grid structures including bridge-like frames, square frames, kagome frames, continuous beams on elastic springs. The frequencies of the topological modes are precisely obtained, applicable to all the bands from low to high frequencies. Continuum lattice grid structures serve as excellent platforms for exploring various kinds of topological phases and demonstrating the topological modes at multiple frequencies on demand. Their topological dynamics has significant implications in safety assessment, structural health monitoring, and energy harvesting.

Analysis of Vibrational Characteristics of the Bank of China Tower in Hong Kong with ANSYS

Abstract. With the rapid urbanization and technological advancements in construction, modern high-rise buildings have become a symbol of urban development. However, these structures face unprecedented challenges due to their increasing height and the complex dynamic loads they must withstand. This paper presents a comprehensive analysis of the Bank of China Tower in Hong Kong, a prominent architectural landmark, using ANSYS software to perform modal and transient structural analyses. The primary structure of the tower consists of five steel-reinforced concrete columns, designed to resist dynamic responses under extreme conditions such as high wind speeds and seismic activities. The modal analysis identified multiple natural frequencies and vibration modes, providing insights into the dynamic behavior of the structure. The transient structural analysis, conducted under seismic wave loading, demonstrated the tower's admirable performance under specified seismic conditions, highlighting its sufficient safety margins. This study emphasizes the importance of advanced materials and innovative structural designs in ensuring the stability and safety of high-rise buildings. The findings contribute significantly to the field of structural engineering, offering valuable insights for the design and analysis of future high-rise structures.

Frequency line detection in spectrograms using a deep neural network with attention.

In this paper, a frequency line detection network (FLDNet) is proposed to effectively detect multiple weak frequency lines and time-varying frequency lines in underwater acoustic signals under low signal-to-noise ratios (SNRs). FLDNet adopts an encoder-decoder architecture as the basic framework, where the encoder is designed to obtain multilevel features of the frequency lines, and the decoder is responsible for reconstructing the frequency lines. FLDNet includes attention-based feature fusion modules that combine deep semantic features with shallow features learned by the encoder to reduce noise in the decoder's deep feature representation and improve reconstruction accuracy. In addition, a composite loss function was constructed by using the continuity of frequency lines, which improved the detection performance of frequency lines. After training through simulated signal sets, FLDNet can effectively detect frequency lines in spectrograms of simulated and measured signals. The experimental results indicate that FLDNet is superior to other state-of-the-art methods, even at SNRs as low as -28 dB.

Predicting the intelligibility of Mandarin Chinese with manipulated and intact tonal information for normal-hearing listeners.

Objective indices for predicting speech intelligibility offer a quick and convenient alternative to behavioral measures of speech intelligibility. However, most such indices are designed for a specific language, such as English, and they do not take adequate account of tonal information in speech when applied to languages like Mandarin Chinese (hereafter called Mandarin) for which the patterns of fundamental frequency (F0) variation play an important role in distinguishing speech sounds with similar phonetic content. To address this, two experiments with normal-hearing listeners were conducted examining: (1) The impact of manipulations of tonal information on the intelligibility of Mandarin sentences presented in speech-shaped noise (SSN) at several signal-to-noise ratios (SNRs); (2) The intelligibility of Mandarin sentences with intact tonal information presented in SSN, pink noise, and babble at several SNRs. The outcomes were not correctly predicted by the Hearing Aid Speech Perception Index (HASPI-V1). A new intelligibility metric was developed that used one acoustic feature from HASPI-V1 plus Hilbert time envelope and temporal fine structure information from multiple frequency bands. For the new metric, the Pearson correlation between obtained and predicted intelligibility was 0.923 and the root mean square error was 0.119. The new metric provides a potential tool for evaluating Mandarin intelligibility.

Beam Dynamics in the Heavy Ion Injector LU2 for the Synchrotron Research Complex (SRC)

The project of the complex for studying ionizing radiation exposure from outer space based on the synchrotron accelerator of protons and various types of ions up to 209Bi is being developed at the Russian Federal Nuclear Center - All-Russian Research Institute ofExperimental Physics (FSUE RFNC - VNIIEF). The accelerator includes two injection complexes (one of which is the source of protons and light ions, the second - heavy ions), the booster accelerator and the main synchrotron. The pulsed type heavy ions linac is being developed at the NRC “Kurchatov Institute” - KCTEF (Kurchatov Complex for Theoretical and Experimental Physics). The ions with mass-to-charge ratio 4÷8 with current of 10 mA will be accelerated up to 4 MeV/u. The linac is proposed, including the RFQ and two DTL sections operating at multiple frequencies. Each DTL section is modular and consists of individually phased H-type resonators (IH-DTLs). Quadrupole lenses located between the resonators for the beam focusing. This DTL structure ensures the accelerator compactness and allows section-by-section configuration and sequential commissioning. 6D beam matching between all sections of the linac is carried out. The results ofbeam dynamics simulation in linear accelerator are presented.

Two-Step Iterative Medical Microwave Tomography.

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT.

Comparison of EEG signal statistical parameters between healthy and SARS-CoV-2 affected individuals

This paper proposes a methodology for selecting and analyzing electroencephalographic (EEG) signals to compare patients with neurological changes due to SARS-CoV-2 infection with healthy individuals. The processing approach involves multiple steps, including windowing, filtering, and frequency analysis, all applied to the EEG data. These methods ensure a clear distinction between healthy and affected brain activity. After processing, key statistical parameters are extracted, averaged, and visualized to highlight the differences between the two groups. Specifically, skewness, kurtosis, and dominant frequency show notable variations. Skewness measures the asymmetry of the signal, kurtosis reflects the sharpness of the peaks, and dominant frequency captures the most prominent oscillations in the brain's activity. The analysis reveals that these parameters significantly differ between healthy individuals and those with neurological changes due to the virus. These differences can provide insights into the neurological impacts of SARS-CoV-2, offering a potential basis for further diagnosis and monitoring. Overall, the proposed methodology presents a systematic way to understand and compare the brain function of affected individuals against healthy controls.

Metasurface-enabled multifunctional single-frequency sensors without external power

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical spaces, digital twins, autonomous driving, smart cities and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating the temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or requiring multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems.