Multiband Frequency Research Articles

Open-Source Algorithm for Automated Vigilance State Classification Using Single-Channel Electroencephalogram in Rodents

Accurate identification of sleep stages is essential for understanding sleep physiology and its role in neurological and behavioral research. Manual scoring of polysomnographic data, while reliable, is time-intensive and prone to variability. This study presents a novel Python-based algorithm for automated vigilance state scoring using single-channel electroencephalogram (EEG) recordings from rats and mice. The algorithm employs artifact processing, multi-band frequency analysis, and Gaussian mixture model (GMM)-based clustering to classify wakefulness, non-rapid, and rapid eye movement sleep (NREM and REM sleep, respectively). Combining narrow and broad frequency bands across the delta, theta, and sigma ranges, it uses a majority voting system to enhance accuracy, with tailored preprocessing and voting criteria improving REM detection. Validation on datasets from 10 rats and 10 mice under standard conditions showed sleep–wake state detection accuracies of 92% and 93%, respectively, closely matching manual scoring and comparable to existing methods. REM sleep detection accuracies of 89% (mice) and 91% (rats) align with previously reported (85–90%). Processing a full day of EEG data within several minutes, the algorithm is advantageous for large-scale and longitudinal studies. Its open-source design, flexibility, and scalability make it a robust, efficient tool for automated rodent sleep scoring, advancing research in standard experimental conditions, including aging and sleep deprivation.

A compact multiband frequency reconfigurable antenna integrated with sextuple band artificial magnetic conductor for heterogeneous wireless applications

A compact multiband frequency reconfigurable antenna integrated with sextuple band artificial magnetic conductor for heterogeneous wireless applications

Tunable Multi-Band Linear Frequency Modulated Waveforms Generation Based on Self-Subharmonic Modulating of Optically Injected Semiconductor Laser

Tunable Multi-Band Linear Frequency Modulated Waveforms Generation Based on Self-Subharmonic Modulating of Optically Injected Semiconductor Laser

Tool Wear State Monitoring in Titanium Alloy Milling Based on Wavelet Packet and TTAO-CNN-BiLSTM-AM

To effectively monitor the nonlinear wear variation of tools during the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM). Firstly, vibration signals from the machine tool spindle are acquired and subjected to the wavelet packet transform (WPT) to extract multi-frequency band energy features as model inputs. Then, the CNN and BiLSTM modules capture the features and temporal relationships of the input signals. Finally, introduction of the AM, combined with the TTAO algorithm, automatically extracts deep features, overcoming issues such as local optima and slow convergence in traditional neural networks, thereby enhancing the accuracy and efficiency of tool wear state recognition. The experimental results demonstrate that the proposed model achieves an average accuracy rate of 98.649% in predicting tool wear states, outperforming traditional backpropagation (BP) networks and standard CNN models.

A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.

Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of electroencephalogram (EEG) and electromyography (EMG) to effectively accomplish motor imagery classification tasks. The model first used a variational autoencoder (VAE) network for unsupervised learning of EEG and EMG signals to extract their deep features, and subsequently applied the channel attention mechanism (CAM) to select these deep features and highlight the advantageous features and minimize the disadvantageous ones. Moreover, in this study, multitask learning (MTL) was applied to train the 2M-hBCINet model, incorporating the primary task that is the MI classification task, and auxiliary tasks including EEG reconstruction task, EMG reconstruction task, and a feature metric learning task, each with distinct loss functions to enhance the performance of each task. Finally, we designed module ablation experiments, multitask learning comparison experiments, multi-frequency band comparison experiments, and muscle fatigue experiments. Using leave-one-out cross-validation(LOOCV), the accuracy and effectiveness of each module of the 2M-hBCINet model were validated using the self-made MI-EEMG dataset and the public datasets WAY-EEG-GAL and ESEMIT. The results indicated that compared to comparative models, the 2M-hBCINet model demonstrated good performance and achieved the best results across different frequency bands and under muscle fatigue conditions. The 2M-hBCINet model constructed based on EMG and EEG data innovatively in this study demonstrated excellent performance and strong generalization in the MI classification task. As an excellent end-to-end model, 2M-hBCINet can be generalized to be used in EEG-related fields such as anomaly detection and emotion analysis.

A 3D-Printed Bi-Material Bragg-Based Reflectarray Antenna.

This paper presents a 3D-printed fully dielectric bi-material reflectarray with bandgap characteristics for multi-band applications. To achieve bandgap characteristics, a "1D Bragg reflector" unit cell is used. The latter is a layered structure characterized by a spatial distribution of refractive index that varies periodically along one dimension. By appropriately selecting the dimensions, the bandgap can be shifted to cover the desired frequency bands. To validate this bandgap characteristic, a (121.5 mm × 121.5 mm) with an f/D ratio of 0.5 reflectarray was fabricated. The measured gain at 27 GHz is 27.22 dBi, equivalent to an aperture efficiency of 35.05%, demonstrating good agreement between simulated and measured performances within the frequency range of 26-30 GHz. Additionally, the transparency of the reflectarray was verified by measuring the transmission coefficient, which exhibited a high level of transparency of 0.32 dB at 39 GHz. These features make the proposed reflectarray a good candidate for multi-band frequency applications.

A Gain‐Enhanced Multiband Frequency and Pattern Reconfigurable Antenna for Wi‐Fi 6E and 5G New Radio Wireless Standards

ABSTRACTIn this paper, a multiband hybrid reconfigurable antenna with enhanced gain is reported to support Wi‐Fi 6E, indoor WLAN, and 5G new radio (NR) wireless standards. The reported structure consists of a half‐hexagonal‐shaped radiating element along with two symmetrical rectangular single‐split resonators interconnected via two PIN diodes to achieve multiband frequency and pattern reconfigurability of the proposed antenna and a single‐layer frequency selective surface (FSS) to enhance the gain. By configuring these PIN diodes in three distinct modes, the reported antenna allows for independent reconfigurability to support multipurpose sub‐6GHz and Wi‐Fi 6E (3.3, 3.5, 5.1, 5.3, and 6.5 GHz) wireless standards, respectively. The results also showed that the antenna is capable of maintaining a frequency of 6.5 GHz in all modes while reconfiguring its radiation pattern in three different directions, namely, 265°, 13°, and 337° on the xz plane. The gain of the proposed hybrid reconfigurable antenna is enhanced by an FSS‐based reflector placed below the radiating structure at a distance of to the lowest operating frequency (3.3 GHz), and the gain is enhanced by 2–4 dBi as compared without FSS. The reported hybrid reconfigurable antenna is implemented on an FR‐4 substrate with a depth of 1.6 mm and a relative permittivity of 4.4. For validation of the proposed structure, the experimental results are compared with the simulated results.

Conformal Frequency Selective Surfaces in Radome Design: A Mini Review

In aerospace engineering, radomes play an important role in protecting antenna systems from external interference and minimizing air resistance during flight. However, traditional radomes often fall short of providing the electromagnetic filtering and stealth capabilities important for modern aircraft applications. Conformal Frequency Selective Surfaces (FSS) radomes represent an evolutionary step beyond their conventional counterparts. These structures not only continue to protect antenna structures from environmental adversities, but also adapt seamlessly to non-planar surfaces. This adaptation is not only functional but also covers aerodynamic and aesthetic considerations, improving stealth capabilities and multi-band frequency operability, which are key factors for state-of-the-art communications and radar systems. However, design and implementation of conformal FSS radomes have some challenges. Considering that one of the authors has focused on such challenges for new generation air platforms, this study examines the contemporary advancements in conformal FSS radome technology, highlighting the latest research and breakthroughs. It covers latest findings on cutting-edge materials, design methodologies, simulation techniques for performance prediction, and the practical applications and advantages of these radomes. Building on a theoretical foundation of FSS and radome integration, the article discusses material and design innovations, simulation challenges, and practical implementations. It is hoped that this review will act as a bridge for knowledge sharing and a stimulant for continued research within the domain of conformal FSS radome technology

Embedded system controlled switching system for reconfigurable antennas

AbstractA multiband and reconfigurable antenna is the one of the main aspects in the communication system which has an extremely important role. An embedded based solution for the fast operation of a reconfigurable antenna to work in a specific operating frequency band is the solution of future technologies. Embedded based solution increases the speed by avoiding the dependence on manual control of the RF switching elements. In this paper, the proposed embedded system‐controlled switching mechanism operate the reconfigurable antenna in any one of the operating bands by a single command to be send from a remote location. The embedded system receives the command and configure a set of PIN diodes as per the modes. The proposed solution consists an Arduino UNO board as motherboard, SIM900A as GSM module to receive the command from user, biasing circuit to control the operation of PIN diode, PIN diode to reconfigure the patch pattern and a user mobile for sending AT command.The proposed model is successful and tested on proteus software 8.0. The model is also deployed on the newly designed multiple‐input‐multiple‐output antenna where the result shows the successful operation to achieve multiband frequency operation.

Dual Polarized Reconfigurable MIMO Antenna for Multi-Band Functioning

This work presents the development, fabrication, and measurements of a new Ultra-wideband sensing multi-band frequency reconfigurable MIMO Active Integrated Antenna (AIA). The proposed antenna has two frequency reconfigured (switching element PIN diodes) planar inverted F-shape antennas (PIFA) using meandered line (ML) structures and a Mirror-shape UWB sensing antenna. The Mirror-shape UWB sensing antenna is integrated with 2x2 MIMO Meandered line PIFA elements for spectrum sensing. PIN diodes (BAR 6402V) are used to provide all digital switching on/off combination modes to attain frequency reconfigurability. Varactor diodes are used for tuning over the wide band. The proposed antenna senses a wide frequency band from 0.5GHz-3GHz and provides frequency reconfigurability over the five bands based on BARP6402V switching modes (0.78GHz-1.01GHz, 1.34GHz-1.62GHz, 1.85GHz-2.18GHz, 2.44GHz-2.67GHz, and 2.76GHz-2.80GHz). All the measured resultant performance parameters are compared with simulation software results. The antenna system is fabricated on an FR4 substrate of size 120mmX65mmX1.6mm. The proposed method used to achieve frequency reconfigurable ultra-wideband MIMO provides a high gain 6.15dBi at a frequency of 1.75GHz. The research also analyzed and investigated all 2x2 MIMO resultant parameters like Total Active Reflection Coefficient (TARC), Isolation coefficient, Envelope correlation Coefficients (ECC), Diversity Gain (DG), Multiplexing efficiency, etc. The proposed antenna is applicable for sensing, communication, & UWB sensing, and cognitive radio (CR) applications since it exhibits stable excellent gain and Omni-directional radiation pattern over the desired bands. Because of its reconfigurable nature, the same antenna also supports use in multiple general-purpose applications like GPS tracing, LTE, UMTS, GSM1800, rescue operation, Wi-Fi/Bluetooth/ISM/WLAN, CDMA, etc applications.

An ultra‐miniaturized single‐layer multiband frequency selective surface with vent for wireless communication

SummaryA novel single‐layer ultra‐miniaturized multi‐band Frequency Selective Surface (FSS) with a circular vent is designed for shielding LTE and ISM bands. The top layer of the proposed FSS unit cell incorporates a circular patch along with convoluted arms. The designed FSS unit cell exhibits band‐rejection characteristics with a resonance of 800 MHz, 2.45 GHz, and 5 GHz and bandwidths of 523 MHz, 687 MHz, and 725 MHz, respectively. The periodicity of an ultra‐miniaturized FSS unit cell is 0.024λօ x 0.024λօ, encompassing a spatial vent hole of 0.01λօ, where λօ corresponds to the lowest operating frequency. Moreover, the proposed FSS unit cell remains unaltered angular stability up to 75° with less than 1% frequency deviation in TE and TM modes. Good polarization insensitivity for the designed four‐fold symmetrical FSS unit cell is achieved. Further, an Equivalent Circuit Model (ECM) analysis is developed to infer the physical insight of the proposed FSS unit cell. The FSS prototype is measured for the assertion, and the good agreement between the findings from simulation and measurement is validated. For real‐time assertion, the FSS prototype is verified using the Amitech SDR04. Thus, FSS is a promising candidate with good ventilation for shielding the LTE, Bluetooth, and Wi‐Fi application bands.

Multiband Frequency Compact Patch Antenna for 5G Applications

The design of a multi-band microstrip patch antenna, with a focus on the frequencies of 27.4GHz, 46GHz, and 58.2GHz, is described in this work. The antenna arrangement consists of a rectangular patch with an incorporated 0.088mm x 2mm rectangular slit. The antenna is based on a 3.285mm×7.235mm Roger RT/duroid 5880(tm) substrate with a dielectric constant of 4.4. It is fed via a 3.4mm×4.1mm microstrip-fed rectangular patch. The antenna operates in multi-band mode, as demonstrated by the simulation results, which show that it can reach respective bandwidths of 19 GHz and 12 GHz. At 27.4 GHz, 46 GHz, and 58.3 GHz, the return loss values are roughly -35.5 dB, -13.35 Db, and - 26.9 dB. The measured gains at 27.4GHz, 46GHz, and 56.6GHz are 6.04dB, 3.55dB, and 10.04dB, respectively. The outcomes produced by this suggested antenna should meet the requirements for 5G wireless communication applications and automotive radar systems.

The visual motion blur elimination method for silicon nitride bearing roller fissures based on U-Net asymmetric multi-scale feature fusion

The visual motion blur imaging for the feature recognition process of silicon nitride bearing roller fissures is a pathological problem. This is solved by proposing squeeze-and-excitation asymmetric fusion of multi-scale features with high-frequency loss attention coupled U-Net (MHU-Net). The visual motion blur elimination of fissure features on silicon nitride bearing rollers is achieved. In the deblurring model, the multi-scale feature information on silicon nitride bearing roller fissures is blocked and there is weak correlation between channels. A design for an asymmetric fusion multi-scale feature module under the channel information compression–excitation mode is proposed. It successfully balances the channel information from different scales while integrating multi-scale features in image fusion. The high-frequency region of fissure features on silicon nitride bearing rollers is analyzed. Around the high-frequency feature loss in the multi-frequency domain of images combined with spatial feature loss, a multi-frequency band high-frequency loss attention module is built. Then, the complete structural details of silicon nitride bearing roller fissures are obtained. The proposed algorithm achieves a peak signal-to-noise ratio of 27.58 and a structural similarity of 0.847 on our self-made silicon nitride defect motion dataset. The visual motion blur of fissure features is noticeably eliminated. The restored image exhibits complete details in the feature structures and overall region smoothness.

Robust multi-frequency band joint dictionary learning with low-rank representation

Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/inter-class local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets.

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

Litar Setara Dwi Jalur Antena Dwikutub Bersepadu untuk Aplikasi RFID

This paper presents dual band antenna by designing basic dipole antenna integrated with C-shaped patches structure for RFID applications. This C-shaped patch is inspired by E-shapes patch antenna that able to perform multiband frequency. The design of the equivalent circuit started with the fundamental circuit of impedance Zo and Z1 which functioned as a dipole antenna in single band. The parallel RLC circuit of impedance Z2 is added in the circuit as loaded patch has affected the resonating frequency. Variations of C1 and L1 on impedance Z1 effect a gap between the two resonance frequencies without changing the bandwidth by shifting the lower frequency. In addition, the variation of C2 and L2 on the impedance Z2 also affects the ratio between the two resonance frequencies but both frequencies are shifted and the bandwidth changes. Consequently, the dual band is presented at 0.915 GHz (UHF RFID) and 2.4 GHz (ISM RFID) by calculating the value of the resistance, inductance and capacitance. The data from electromagnetic simulation are compared with the data predicted by the antenna circuit modeling. The frequency sampled measurement data may be represented in the form of S-parameter in order to represent dependent data in a time domain simulator in P-SPICE software. The C-shaped design can be independently controlled to perform lower band and upper band by adjusting the width and length of the patch. The performance of the proposed antenna demonstrates the dual band antenna for RFID application with good agreements between measured and simulated results.

Effective multiband synthetic four-wave mixing by cascading quadratic processes

Four-wave mixing (FWM) is an important technique for supercontinuum and frequency comb generation in the mid-infrared band. Here, we report simultaneous synthetic FWM in both the visible and mid-infrared bands by cascading quadratic nonlinear processes in a periodically poled lithium niobate (PPLN) crystal, which has a conversion efficiency that is 110 dB (at 3000 nm) higher than the FWM generated directly using third-order susceptibilities in bulk PPLN crystals. A general model of the proposed process is developed that shows full agreement with the experimental verification results. The frequency difference between the emerging frequency components can be tuned freely by varying the frequency difference between the dual pump lasers. Furthermore, by increasing the conversion bandwidth and the efficiency of the cascaded processes, it becomes feasible to generate frequency combs simultaneously in three bands, comprising the visible, near-infrared, and mid-infrared bands, via high-order cascaded processes. This work represents a route toward free-tuning multiband frequency comb generation with multi-octave frequency spanning that will have significant applications in fields, including mid-infrared gas sensing, lidar, and high-precision spectroscopy.

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user’s intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

A Reconfigurable Local Oscillator Harmonic Mixer with Simultaneous Phase Shifting and Image Rejection

The multibeam high-throughput satellites (HTS) are regarded as a crucial component in the forthcoming space-based Internet of Things (S-IoT) network. The multi-band frequency conversion capability of HTS is essential for achieving high-capacity information interconnection in the S-IoT network. To enhance the frequency conversion capability of the on-board payload, a reconfigurable local oscillator (LO) harmonic mixer with simultaneous phase shifting and image-rejection is proposed and demonstrated based on a polarization division multiplexing dual-parallel Mach–Zehnder modulator (PDM-DPMZM). By adjusting the input radio frequency (RF) signal and direct current (DC) bias voltage of the modulator, four different LO frequency-multiplication mixing functions can be achieved. The phase of the generated signal can be flexibly tuned over a full 360° range by controlling the angle α between the polarization direction of the polarizer and one axis of the modulator, and it has a flat amplitude response. When combined with an optical frequency comb, the scheme can be extended to a multi-channel multi-band frequency conversion system with an independent phase tuning capability. Additionally, by adjusting the phase difference between dual channel output signals, it can be reconfigured to implement in-phase/quadrature (I/Q) mixing, double-balanced mixing and image-reject mixing.

A novel linear and planar multiband fractal‐shaped antenna arrays with low sidelobes

SummaryFractal antenna arrays are usually used to tune multiband frequencies. However, these types of iteratively constructed antennas are associated with undesirable high sidelobe levels and low directivities. In this paper, an optimization procedure based on the genetic algorithm is used to find the optimal weights of the array elements such that the corresponding array patterns have low sidelobe levels and good directivity. Moreover, the fractal nature in the proposed arrays is maintained regardless of the optimized weights. Thus, the proposed fractal‐shaped array maintains its capability in performing multiband frequency operation. These good radiation features make the proposed fractal‐shaped array more appropriate for the current and future wireless communication applications. Simulation results confirm the superiority of the presented linear and planar fractal‐shaped array structures with compared to the conventional fractal cantor linear array and the standard Sierpinski carpet planar array. For the proposed fractal cantor linear array, the sidelobe level has been reduced to more than −20 dB at different operating frequencies, and the directivity has been improved by more than 8 dB, while the modified Sierpinski carpet planar array has achieved −30 dB depressions in the sidelobe level and 6 dB improvement in the directivity.