Target Frequency Research Articles

BEM-based fast frequency sweep for acoustic scattering by periodic slab

This paper presents a boundary element method (BEM) for computing the energy transmittance of a singly-periodic grating in 2D for a wide frequency band, which is of engineering interest in various fields with possible applications to acoustic metamaterial design. The proposed method is based on the Padé approximants of the response. The high-order frequency derivatives of the sound pressure necessary to evaluate the approximants are evaluated by a novel fast BEM accelerated by the fast-multipole and hierarchical matrix methods combined with the automatic differentiation. The target frequency band is divided adaptively, and the Padé approximation is used in each subband so as to accurately estimate the transmittance for a wide frequency range. Through some numerical examples, we confirm that the proposed method can efficiently and accurately give the transmittance even when some anomalies and stopband exist in the target band.

Target-oriented inverse optimization design of phononic crystals with variable constraints for vibration attenuation

Previous research on phononic crystals (PnCs) design mostly focused on the maximum band gap (BG) between adjacent orders and could not optimize the desired target frequency based on actual engineering problems. In order to achieve an arbitrarily specified BG structure of PnCs near the target frequency, a new topology design method for PnCs is proposed using a special triangular lattice PnC as the object. In the genetic algorithm (GA) optimization framework, two variable constraint optimization strategies, fixed-step and adaptive, are proposed to form a variable constraint genetic algorithm (VC-GA), which enables the non gradient optimization process to be continuous and uninterrupted. This optimization model also divides optimization into two stages, which can search for target individuals and maximize BG under the constraints of the target BG. Combining the optimization model with the improved fast plane wave expansion method (IFPWEM) for optimizing the single and multi-objective BGs in the in-plane and out-of-plane modes, and the weight coefficient is introduced to broaden its scope of application. The effectiveness of the method is verified through experiments, and the vibration reduction optimization design is carried out within a specific frequency range for the engineering structure corrugated plate as an example.

Broadband metasurfaces for steering flexural waves in thin plates: A topology optimization approach

Flexural vibrations in thin plates are prevalent in practical engineering applications, and significant efforts have been dedicated to controlling these vibrations in recent years. However, steering of broadband flexural vibrations/waves in thin plates remains challenging. In this study, we propose a topology optimization-based technique to design broadband metasurfaces to steer flexural waves in plate-like structures. Broadband performance is achieved by formulating fitness functions that incorporate results at uniformly distributed sampling frequencies within the target frequency range. As an illustration, two refractive metasurfaces with a relative bandwidth of 20% and 60% are designed and characterized numerically. The first metasurface is further fabricated using 3D printing and experimentally tested under two different signal excitations. Both numerical simulations and experiments validate the efficacy of our designs. In addition, we conduct a comparative analysis with two counterparts designed through experience-based methods, highlighting the evident advantages of employing topology optimization for broadband metasurface design. This work proposes a topology optimization-based approach for designing broadband elastic metasurfaces.

Topology optimization of two-dimensional magnetorheological elastomer phononic crystal plate with tunable bandgap considering a specified target frequency

Topology optimization of two-dimensional magnetorheological elastomer phononic crystal plate with tunable bandgap considering a specified target frequency

Posaconazole in paediatric malignancy and haematopoietic stem cell transplant: dosing to achieve therapeutic concentration.

Posaconazole is increasingly used for the treatment and prophylaxis of invasive fungal infections in immunocompromised children. We aimed to review evidence for paediatric posaconazole dosing regimens focusing on attainment of target concentrations and frequency of adverse effects. In May 2023, the Cochrane, Embase, MEDLINE and PubMed databases were searched for articles reporting posaconazole dosing in children with malignancy or post-haematopoietic stem cell transplantation. Studies reporting the attainment of target serum concentrations were included. Overall, 24 studies were included. Eighteen studies of the oral suspension consistently reported poor attainment of target concentrations for prophylaxis (≥0.7 µg/mL, 12%-78%) despite high daily doses of 14-23 mg/kg/day (max. 1200 mg/day). Target attainment was significantly affected by gastric pH and food intake. Six studies of the delayed-release tablet (DRT) reported 58%-94% achieved concentrations ≥0.7 µg/mL, with the majority using lower doses of 4-12 mg/kg/day (max. 300 mg/day). Similarly, one study of powder for oral suspension found 67%-100% achieved target concentrations with a dose of 6 mg/kg/day (max. 300 mg/day). As expected, the IV formulation had high attainment of prophylaxis targets (81%-90%) with 6-10 mg/kg/day (max. 400 mg/day). All formulations were well tolerated, and no relationship between adverse effects and posaconazole concentrations was identified. The required posaconazole dose in immunocompromised children varies depending on the formulation. The IV infusion had the highest attainment of therapeutic concentration followed by the DRT and powder for suspension. By contrast, the oral suspension had low attainment of target concentrations despite higher daily doses.

Deep learning-aided topology design of metasurfaces for Rayleigh waves

Abstract Metasurfaces can effectively attenuate Rayleigh waves propagating in soil, safeguarding structures from ambient vibrations or earthquakes. However, there remains a lack of efficient approaches for designing metasurfaces that can isolate Rayleigh waves within desired frequency ranges under different site conditions. This study presents a deep learning (DL)-based topology optimization method for designing metasurfaces isolating Rayleigh waves in a target frequency range, which has potential applications for surface wave control. The proposed DL model employs a variational autoencoder to transform high-dimensional and discrete topologies into low-dimensional and continuous latent vectors, reducing the design difficulty. On this basis, the conditional tandem neural network is constructed to optimize latent vectors under different soil conditions, improving the design efficiency and verifying the universality of the method. The design reliability of the DL model is validated through 100 tests with determination coefficients more than 0.99. In addition, the generations of different metasurfaces for the same target are explored, providing designers with more choices. The insulation capabilities of the designed metasurfaces against Metro-induced Rayleigh waves are demonstrated from the time- and frequency-domain responses. The presented DL-aided design method provides a novel insight into the customization of metasurfaces for manipulating Rayleigh waves.

Efficient design of sandwich panels with cellular truss cores and large phononic band gaps using surrogate modeling and global optimization

Recent advancements in additive manufacturing technologies and topology optimization techniques have catalyzed a transformative shift in the design of architected materials, enabling increasingly complex and customized configurations. This study delves into the realm of engineered cellular materials, spotlighting their capacity to modulate the propagation of mechanical waves through the strategic creation of phononic band gaps. Focusing on the design of sandwich panels with cellular truss cores, we aim to harness these band gaps to achieve pronounced wave suppression within specific frequency ranges. Our methodology combines surrogate modeling with a comprehensive global optimization strategy, employing three machine learning algorithms—k-Nearest Neighbors (kNN), Random Forest Regression (RFR), and Artificial Neural Networks (ANN)—to construct predictive models from parameterized finite element (FE) analyses. These models, once trained, are integrated with Particle Swarm Optimization (PSO) to refine the panel designs. This approach not only facilitates the discovery of optimal truss core configurations for targeted phononic band gaps but also showcases a marked increase in computational efficiency over traditional optimization methods, particularly in the context of designing for diverse target frequencies.

Low mutual coupling miniaturized dual-band quad-port MIMO antenna array using decoupling structure for 5G smartphones

Maintaining the compactness of 5G smartphones while accommodating millimeter-wave (mm-wave) bands presents a significant challenge due to the substantial difference in frequency. To tackle this issue, we introduce a miniaturized quad-port dual-band multiple-input, multiple-output (MIMO) antenna with low mutual coupling (MC) and a considerable frequency difference. This quad-port MIMO antenna, built on a Rogers TMM4 substrate, measures 17.76 × 17.76 mm2 and boasts a dielectric constant of 4.5. It incorporates four planar patch antennas, positioned at the corners in perpendicular orientations. For dual-band operation at 28/38 GHz, each antenna element features a rectangular patch with four rectangular slots, complemented by a full ground plane. The spacing between these elements is 0.5 λo, and we've included a decoupling structure (DS) to minimize mutual coupling (MC) among the MIMO antenna elements with minimal complexity and cost. Simulation and measurement results reveal a significant reduction in mutual coupling between the array elements, ranging from − 25 to − 60 dB. As a result, we’ve developed the envelope correlation coefficient (ECC) and made advancements in the total active reflection coefficient (TARC), mean effective gain (MEG), and diversity gain (DG). The measured gains for this design are approximately 8.9 dBi at both 28 GHz and 38 GHz, with a radiation efficiency of nearly 93%. Furthermore, specific absorption rate (SAR) analysis confirms the MIMO antenna's suitability for smartphone handsets operating within the target frequency band.

Targeting Frequency of Gait Training in the Motor Complete Cervical Spinal Cord Injury Population: A case series

Targeting Frequency of Gait Training in the Motor Complete Cervical Spinal Cord Injury Population: A case series

Acoustic metasurface embedded with thin-walled plate based on phase modulation for multi-angle broadband sound absorption

The design of low-frequency broadband acoustic absorbers with thin thicknesses is a longstanding and challenging problem. This work proposes an acoustic metasurface consisting of macro-perforated porous, thin-walled perforated plate, and meta-porous layers embedded by thin-walled plate (MPM) to combine the advantages of porous material and resonator. MPM is formed by four subunits with a phase gradient. The acoustic properties of an MPM unit cell are investigated by a theoretical model based on homogenization theory and transfer matrix methods, and the theoretical model is verified by a numerical model. Then, the sound absorption mechanism of MPM unit cell is explored, and it is revealed that the mid-frequency and low-frequency sound waves are mainly dissipated by thin-walled perforated plate and meta-porous layers, and the macro-perforated porous structure target high-frequency absorption. In addition, the dependence of geometric parameters of MPM unit cell on acoustic performance and reflected phase is investigated. Furthermore, the geometric parameters of each subunit in acoustic metasurface are carefully designed, and excellent absorption in target frequency is obtained by the hybrid resonance of a single unit cell and the surface wave conversion of MPM with reflected phase gradient. Also, the appearance of perfect sound absorption comes from the far-field interference between reflected waves and the wave-trapping phenomenon near the periodic structure. A thin (λ/20) absorption panel MPMs is designed to achieve multi-angle low-frequency broadband absorption and an effective normal sound absorption (>50 %) band over 344–3000 Hz. This work proposes a novel design idea and opens possibilities for acoustic metasurface practical applications in noise mitigation of various scenarios.

Efficient broadband sound absorption exploiting rainbow labyrinthine metamaterials

In this work, we demonstrate in a proof of concept experiment the efficient noise absorption of a 3D printed panel designed with appropriately arranged space-coiling labyrinthine acoustic elementary cells of various sizes. The labyrinthine unit cells are analytically and numerically analysed to determine their absorption characteristics and then fabricated and experimentally tested in an impedance tube to verify the dependence of absorption characteristics on cell thickness and lateral size. The resonance frequency of the unit cell is seen to scale approximately linearly with respect to both thickness and lateral size in the considered range, enabling easy tunability of the working frequency. Using these data, a flat panel is designed and fabricated by arranging cells of different dimensions in a quasi-periodic lattice, exploiting the acoustic ‘rainbow’ effect, i.e. superimposing the frequency response of the different cells to generate a wider absorption spectrum, covering the target frequency range, chosen between 800 and 1400 Hz. The panel is thinner and more lightweight compared to traditional sound absorbing solutions and designed in modular form, so as to be applicable to different geometries. The performance of the panel is experimentally validated in a small-scale reverberation room, and an absorption close to ideal values is demonstrated at the desired frequencies of operation. Thus, this work suggests a design procedure for noise-mitigation panel solutions and provides experimental proof of the versatility and effectiveness of labyrinthine metamaterials for tunable mid- to low-frequency sound attenuation.

Structural rotor rub-impact diagnosis under intricate noise interferences based on targeted component extraction and stochastic resonance enhancement

Rub-impact is a common nonlinear fault of the rotor system, occurring in rotating machines with radial clearance between the rotor and the stator, which may lead to serious consequences. Since the vibration response of rotor rub-impact is shown as multicomponent with time-varying characteristics of undulatory instantaneous frequency, it is desired to exploit advanced signal processing methods for rub-related feature excavation and failure diagnosis under complex noise interferences, which is of crucial significance to ensure the stable and efficient operation of the whole unit. This paper concerns the processing of acceleration signals and proposes a novel intrawave frequency modulation detection approach for structural rotor rubbing diagnosis based upon targeted component extraction and stochastic resonance enhancement. First, the acquired vibratory acceleration signal is converted into displacement signal via a two-stage integration strategy. Next, to extract the rotating frequency component of high information clarity for further time–frequency analysis from the multicomponent signal, an especially designed improved variational mode decomposition method based on the modified target frequency index is put forward, and the instantaneous frequency of the objective component is estimated. Then, the optimum stochastic resonance is leveraged for intrawave frequency modulation enhancement. Finally, the rotor rub-related symptom can be distinctly revealed and the diagnostic procedure can be performed. The effectiveness and superiority of the proposed rotor rub-impact diagnosis approach are demonstrated through both simulations and experiments, indicating that it is suitable to be implemented in practical applications, with high noise-resistance ability, and can efficiently extract the potential characteristics of rotor rub-impact malfunction from multicomponent signals.

High-Order Temporal Convolutional Network for Improving Classification Performance of SSVEP-EEG

Background and objectiveSteady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) aim to detect target frequencies corresponding to specific commands in electroencephalographic (EEG) signals by classification algorithms to achieve the desired control. However, SSVEP signals suffer from low signal-to-noise ratio and large differences in brain activity. Moreover, the existing CNN models have small receptive fields, which make it difficult to receive large range of feature information and limit the effectiveness of classification algorithms. MethodsTo this end, we proposed a high-order temporal convolutional neural network (HOT-CNN) model for enhancing the performance of SSVEP target recognition. Specifically, the SSVEP-EEG signals was divided into equal-length time segments and a time-slice attention module was designed to capture the correlation between time slices. The module improves the local characterization of signals and reduces biological noise interference by automatically assigning high weights to locally relevant temporal sampling cues and lower weights to other temporal cues. Moreover, for global features, a temporal convolutional network module was designed to increases the receptive field of the network and to extract more comprehensive time domain features by using dilated causal convolution. Finally, the fusion and analysis of local and global features are achieved by designing a feature fusion and classification module to accomplish accurate classification of SSVEP signals. ResultsOur method was evaluated on large publicly available datasets containing 35 subjects and 40 categories. Experimental results indicated that HOT-CNN achieved encouraging performance compared with other advanced methods: the highest information transfer rate of 241.01bits/min was obtained using 0.5s stimuli, and the highest average accuracy of 96.39% was obtained using 1.0s stimuli. ConclusionsThe method effectively reinforced the global and local time-domain information and improved the classification performance of SSVEP, which has wide application prospects.

Designing ultra-broadband terahertz polarization converters based on the transformer model

Polarization, a fundamental property of terahertz (THz) waves, can be controlled using metasurfaces for signal transmission and sensitive measurements. However, conventional methods for designing polarization-conversion metasurfaces require numerous numerical simulation iterations and trial and error attempts that are computationally intensive and time-consuming, necessitating a more rapid and efficient approach. This study proposes a transformer-based method for the inverse design of terahertz polarization converters; the approach can accurately design the corresponding metasurface based on the target spectrum. The model also comprises a forward network that can precisely and intuitively predict the meta-atoms spectra with high simulation speed and reduced computational cost. Using this approach, two ultra-broadband polarization conversion devices were designed in approximately 0.006 s within a target frequency range of 0.5–4 THz, with polarization conversion ratios greater than 90% within 1.23–3.19 THz and 1.18–3.24 THz. Additionally, the average cross-polarization ratio was greater than 50%. This study provides insights into designing terahertz polarization converters, which are applicable in terahertz communications, polarization imaging, and biomedical procedures.

Measuring and modeling Gaussian noise disruption across frequency, level, and hearing status

Recent work suggested that older listeners with minimal hearing loss exhibit more forward masking for a highly fluctuating Gaussian noise (GN) masker relative to a low-fluctuation noise (LFN) masker. This threshold difference (GN–LFN) is referred to as GN disruption and was previously attributed to time-varying cochlear gain resulting from feedback control by the medial olivocochlear (MOC) system. However, thus far, masker conditions have been limited to 80 dB SPL at 4 kHz. The current study aims to challenge the hypothesis that the physiological basis of GN disruption is the MOC system using behavioral measures and computational models across multiple target and masker frequencies (0.5, 1, 2, and 4 kHz), masker levels (80-, 65-, and 50-dB SPL), masker-signal delays (25- and 75-ms), participant age ranges (18–30 and 60–75), and hearing statuses (normal hearing, sensorineural hearing loss). Additionally, speech recognition using IEEE sentences will be measured with GN and LFN maskers. Although testing will not be completed until 2025, preliminary data from 20 younger participants with normal hearing suggest that GN disruption is greater for higher than lower masker and probe frequencies, that GN disruption is similar across masker levels, and that refinements to the computational model are needed to more accurately predict GN disruption.

Research on Implementation of a PWM Generation Algorithm for Train Stationary Stopping Frequency

In industrial electronic equipment or communication equipment, a reference clock should be generated for stable operation of the equipment, which requires precise and stable reference frequency generation. As a method for generating this reference frequency, an analog method called PLL (phase locked-loop) has been devised and widely used. However, in order to make a more precise and stable reference frequency simple and economical, a DDS (direct digital synthesizer) has been developed. In this paper, we propose a stable and accurate method to generate a low frequency of the PWM method via pure logic circuit configuration without a microprocessor for digital reference frequency generation. Depending on the electronic communication equipment, the required reference frequency varies from a low frequency to a very high frequency. The reference frequency synthesis required in these frequency bands has been studied in various ways, but in industries such as railways, the low-frequency band based on the DDS method is used. In particular, it is very important to operate without a single operating error or failure in order to obtain information for stopping the train. Therefore, it is necessary to design a pure logic method that excludes a stored program type processor that minimizes the possibility of temporary interruption due to disturbance such as surge or high voltage. Therefore, through this study, the algorithm is implemented so that the duty ratio is output at 50:50, the circuit is configured so that two target frequencies are generated at the same time, and the performance is verified by generating the low-frequency band used for stopping the railway train. It was confirmed that the accuracy and stability were improved compared to the analog method used for stopping the railway train, and it was verified that the frequency resolution was superior to the similar results obtained in the digital frequency synthesis field so far.

Exposure Time Control Method for Higher Intermediate Frequency in Optical Heterodyne Imaging and Its Application to Electric-Field Imaging Based on Electro-Optic Effect.

We propose and demonstrate a method for equivalent time sampling using image sensors to selectively detect only the target frequency. Shortening the exposure time of the image sensor and using equivalent time sampling allows for the detection of frequency components that are higher than the frame rate. However, the imaging system in our previous work was also sensitive to the frequency component at 1/4 of the frame rate. In this study, we control the phase relationship between the exposure time and observed signal by inserting an additional interval once every four frames to detect the target frequency selectively. With this technique, we conducted electric field imaging based on the electro-optic effect under high noise conditions in the low-frequency band to which the conventional method is sensitive. The results demonstrated that the proposed method improved the signal-to-noise ratio.

Perceptions, barriers, and challenges of oral care among nursing assistants in the intensive care unit: a qualitative study

BackgroundAlthough oral hygiene is closely related to various diseases, it is sub-optimal in the Intensive Care Unit (ICU). Oral care in the ICU is challenged by nursing workloads, low staffing, and higher acuity patients, there are few policies and written guidelines for oral care. Nurses often delegate oral care to nursing assistants (NAs) whose role is overlooked. This study is to explore the perspectives, obstacles, and challenges of NAs in the oral care of the ICU.MethodsA qualitative study and semi-structured interviews were conducted with NAs in three ICU units, and Colaizzi’s phenomenological method was used to analyze the records.ResultsInitially, 13 NAs met the inclusion criteria, and two did not participate in this study as they refused to be recorded. Finally, 11 ICU NAs were interviewed, with three receiving face-to-face interviews and eight receiving telephone interviews. Using Colaizzi’s phenomenological method, two themes and eight subthemes emerged from the data, we examined the self-perception, barriers and challenges of NAs regarding oral care and identified the subthemes: (1) The target audience, frequency, and importance; (2) Role; (3) Evaluation; (4) Patient-related factors; (5) Oral care tools; (6) Psychology of NAs; (7) Lack of education and training; (8) Lack of team support.ConclusionNursing assistants whose roles are overlooked by the nursing team are important members of the ICU team. Though oral care is closely related to disease prevention, it is rarely considered an essential task. Major barriers to implementing oral care in the ICU environment and patients include the psychological quality of participants, non-standard education and training, and inadequate team support. The expectation is that medical personnel will prioritize oral hygiene and recognize the significance of NAs in nursing work. Furthermore, future ICU oral care should investigate suitable tools and mouthwashes, simplified and standardized processes, standardized training, and multidisciplinary team collaboration.

Fabrication and characterization of delay lines with spoof surface plasmon polariton waveguide coupled with C-shaped metamaterials for microwave integrated circuits

Abstract Here, we proposed two delay lines consisting of spoof Surface Plasmon Polariton (sSPP) waveguides and C-shaped metamaterials (C-MMs). The delay lines, namely OFF and ON devices, were designed and fabricated. On the OFF device, an sSPP waveguide is capacitively coupled to the C-MMs via an air gap on a high-resistivity silicon substrate. On the ON device, a connection is established between the C-MMs and the sSPP waveguide by metal connectors. The difference in the electrical properties in the coupling between the C-MMs and the sSPP waveguide creates a large phase contrast between the ON and OFF delay lines. The structural design was performed using a numerical calculation based on a commercial finite element solver. We successfully fabricated and characterized delay lines with phase differences equal to tens of degrees between the ON and OFF devices in the target frequency range of 2-6 GHz, while maintaining the original transmittance properties. The promising applications of the delay lines are a phase shifter or modulator when integrating with suitable switches.

Denoising CSAMT signals in the time domain based on long short-term memory

Abstract Controlled-source audio frequency magnetotellurics (CSAMT) is an artificial-source electromagnetic technique that partially mitigates the limitations of weak natural-field signals. However, practical field surveys inevitably encounter strong interference, severely affecting signal quality. Traditional methods such as Fourier transformation, which directly computes apparent resistivity from frequency-domain information, are inadequate in this context, so we need alternative denoising approaches. However, research on CSAMT denoising is currently limited. Given the excellent performance of long short-term memory (LSTM) neural networks in the processing of magnetotelluric (MT) data, as demonstrated by previous studies, this paper proposes the use of LSTM to denoise CSAMT signals in the time domain. Unlike traditional denoising methods, we aim to directly extract the target frequency signal from the time-series data for denoising. For MT data, target frequency signals and noise are all mixed together, so noise suppression can only be achieved by identifying noise characteristics in the time series. However, unlike MT data, CSAMT data has an artificial transmitting source, and the frequency of the valid signal is fixed within a time interval. This allows for the direct extraction of target frequency signals without considering the complex characteristics of noise. In this study, we developed a neural network based on bidirectional LSTM to accomplish the task of noise suppression. After conducting both simulated and measured data tests, this method was able to, on average, improve the signal-to-noise ratio (SNR) of CSAMT data by approximately 20 dB and partially address the challenge of denoising when the data's SNR falls below 0 dB.