Two-dimensional arrays with nonlinear elements

Spectrum sensing is important for a cognitive radio (CR) system to protect primary users (PUs) from harmful interference. At present, most of the existing sensing schemes are proposed in Gaussian noise environments. Nevertheless, a CR system actually also suffers from non-Gaussian noise, such as man-made impulsive noise, ultrawideband interference and co-channel interference. In this article, to handle the degradation of detection performance in non-Gaussian impulsive noise environments, a robust spectrum sensing scheme named hyperbolic tangent-based energy detector (HT-ED) is proposed. The hyperbolic tangent function is applied to suppress the impulsive noise by the nonlinear behavior, but without restraining the normal noise by the linear behavior. This property enables the new method to achieve the good detection performance in both Gaussian and non-Gaussian noise environments. In addition, it is similar to an energy detector, the new detector does not need to know the characteristics of the PU's signal. The performance analysis of the proposed HT-ED scheme is also presented. The simulation results show the HT-ED is robust against impulsive noise, which is modeled as Laplace or α-stable noise. Moreover, the HT-ED provides a superior detection performance compared with the other existing relative methods in a wide range of non-Gaussian noise.

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks

The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter. Then comes the fusion process, which uses an approach that combines NSCT and SR to fuse low frequency components. Similarly, the Max-absolute fusion rule is used to merge high frequency components. Finally, the final image is obtained through the disintegration of fused low and high frequency data. In terms of correlation coefficient, Entropy, spatial frequency, and fusion mutual information, our method outperforms other methods in terms of image quality enhancement and visual evaluation.

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

Rotating machinery fault diagnosis is of great importance to guarantee safe and optimal operations of industrial processes. Heavy noise and dynamic behaviors usually make accurate mechanical fault diagnosis impossible while using the standard methodologies, particularly when they disregard specific domain information related to time, frequency, or space. To address these challenges, we propose a novel model, called spatio-temporal-frequency graph attention network (STFGAT), which can integrate time domain, frequency domain, and spatial information. The model leverages the Transformer to encode time and frequency information, then refined complex patterns in the time and frequency domain through self-attention mechanism and frequency domain attention, and finally captures the hidden patterns behind the data through the collaboration of time and frequency information. The encoded information is subsequently fed into the spatio-temporal graph attention network (STGAT) to allow the model to take full use of the spatial relationships between different components of the mechanical system and the temporal relationships across various time lags. This process improvement can learn complex patterns and relationships within the data, thereby facilitating predictions regarding the system’s state. The experimental results show that STFGAT outperforms other standard diagnostic models in the case studies and can achieve better diagnostic accuracy.

Constrained least lncosh adaptive filtering algorithm

A class of spread-spectrum signals, referred to as ‘continuous phase chirp modulation’ (CPCM), is examined for error rate performance in an impulsive noise environment. Optimum and locally optimum multiple-bit observation algorithms are derived and the latter is analysed for close-form error performance. Optimum weak coherent CPCM signalling systems are determined as a function of sample size and decision observation length of the receiver. It is shown that the optimized CPCM offers a performance equal to that of PSK/MSK. Suboptimum receivers that are easy-to-implement are also analysed and their performance compared with that of the locally optimum receiver.

In this paper, we propose an emotion recognition model based on convolutional neural network (CNN), long short term memory (LSTM) and channel attention mechanism, aiming at the low classification accuracy of machine learning methods and the uneven spatial distribution of electroencephalogram (EEG) electrodes. This model can effectively integrate the frequency, space and time information of EEG signals, and improve the accuracy of emotion recognition by adding channel attention mechanism after the last convolutional layer of the model. Firstly, construct a 4-dimensional structure representing EEG signals. Then, a CLSTM model structure combining CNN and LSTM is designed. CNN is used to extract frequency and spatial information from 4-dimensional input, and LSTM is used to extract time information. Finally, the channel attention module is added after the last convolutional layer of CLSTM model structure to allocate the weight of different electrodes. In this paper, an emotion recognition model based on CLSTM and channel attention mechanism was proposed from the perspective of integrating the frequency, space and time 3-dimensional information of EEG signals. The average classification accuracy of the model on SEED public data set reached 93.36%, which was significantly improved over the existing CNN and LSTM emotion recognition models.

This work presents an approach to the analysis of multiple motions in video, which combines frequency and spatial domain information in a new manner. The tasks of interest are finding the number of moving objects, velocity estimation, object tracking, and motion segmentation. We propose a novel, hybrid approach, which avoids problems of spatial domain methods, like sensitivity to illumination changes or great computational cost, but also uses spatial information for precise object localization. Experiments with synthetic and real sequences, show both the possibilities and limitations of this approach.

Although recent evidence suggests that certain phonetic trading relations have an auditory basis, others appear to be the result of listeners' knowledge of articulatory constraints on production, or of their acoustic consequences. The latter group includes the effect of first‐formant (F1) frequency on the voicing boundary in initial stop consonants; as F1 is lowered, longer voice‐onset times (VOT) are required to perceive stops voiceless. Summerfield [J. Acoust. Soc. Am. 72, 51–61 (1982)] and Hillen‐brand [J. Acoust. Soc. Am. 75, 231–240 (1984)] showed for nonspeech stimuli varying in tone‐onset time (TOT), simultaneity‐successivity judgments did not vary systematically with frequency of the lower tone. This study demonstrates that when listeners learn to associate different frequency components with two nonspeech categories (simultaneous and nonsimultaneous onsets), they will use frequency information in making simultaneity judgments. Two TOT series were generated, consisting of two‐component, steady‐state, sine‐wave stimuli; in each series, the onset of the lower tone (f1) lagged that of the higher tone (f2) by 0–120 ms. For all stimuli, f2 was 1240 Hz; f1 was 750 HZ for one series and 250 Hz for the other. When subjects were trained on the simultaneous endpoint from the high f1 series and the nonsimultaneous endpoint from the low f1 series, subsequent identification performance revealed significantly more stimuli were labeled “simultaneous” for the high‐f1 series than for the low‐f1 series. These results suggest that general principles of perceptual learning can account for the interaction between VOT and f1 onset in specifying voicing categories in initial stops. [Work supported by NICHHD.]

Fine-grained spatial information extraction in radiology as two-turn question answering

Remarkable inter-class similarity and intra-class variability of tomato leaf diseases seriously affect the accuracy of identification models. A novel tomato leaf disease identification model, DWTFormer, based on frequency-spatial feature fusion, was proposed to address this issue. Firstly, a Bneck-DSM module was designed to extract shallow features, laying the groundwork for deep feature extraction. Then, a dual-branch feature mapping network (DFMM) was proposed to extract multi-scale disease features from frequency and spatial domain information. In the frequency branch, a 2D discrete wavelet transform feature decomposition module effectively captured the rich frequency information in the disease image, compensating for spatial domain information. In the spatial branch, a multi-scale convolution and PVT (Pyramid Vision Transformer)-based module was developed to extract the global and local spatial features, enabling comprehensive spatial representation. Finally, a dual-domain features fusion model based on dynamic cross-attention was proposed to fuse the frequency-spatial features. Experimental results on the tomato leaf disease dataset demonstrated that DWTFormer achieved 99.28% identification accuracy, outperforming most existing mainstream models. Furthermore, 96.18% and 99.89% identification accuracies have been obtained on the AI Challenger 2018 and PlantVillage datasets. In-field experiments demonstrated that DWTFormer achieved an identification accuracy of 97.22% and an average inference time of 0.028 seconds in real plant environments. This work has effectively reduced the impact of inter-class similarity and intra-class variability on tomato leaf disease identification. It provides a scalable model reference for fast and accurate disease identification.

Convolutive blind source separation and adaptive beamforming have similar goals and similar system structure. Both attempt to extract selected source signals from observed sensor mixtures by a filter array. However, time and frequency information are utilized in convolutive blind source separation while spatial information of source signals or sensor array is used in adaptive beamforming. In this paper, we start with a brief introduction of blind source separation and adaptive beamforming. Next, we review approaches combining spatial information used in beamforming with time/frequency processing used in convolutive blind source separation. We also present a new proposed combination approach and simulation results.

Color histograms are widely used for content-based image retrieval because they are trivial and effective to compute, not sensitive to size and orientation, and robustly tolerate movement of objects in the image and changes in camera viewpoint. However, a color histogram only records an image's overall color compositions and no any spatial information is included, and also retrieval precision is limited. In this paper, a color-spatial histogram, which incorporates spatial information with color compositions without sacrificing the robustness of color histograms, is proposed. The purpose is considering the representation role of color compositions and spatial information in the meantime. Each entry in a color-spatial histogram is the color frequency and corresponding position information of the virtual boundary. Experimental evidence suggests that this new color-spatial histogram outperform not only the traditional color histogram method but also the other color-spatial histogram methods for image retrieval.

This monograph presents innovative methods for constructing two-dimensional non-equidistant antenna arrays (AAs), particularly designed for radio telescopes operating in the 8–80 MHz range. The work introduces direct and straightforward techniques, emphasizing configurations based on Latin squares and their triangular matrices. These methods ensure full spatial frequency coverage with high rarefaction and minimal lateral radiation. Key contributions include the first use of a Latin square matrix in AA construction, the pioneering combination of circular difference sets and Latin square matrices for planar sparse arrays, and the integration of Latin square and lower triangular matrices in planar sparse array design. The proposed matrix method is more efficient and direct compared to traditional iterative and deletion-based optimization approaches. This book is intended for scientists and specialists involved in developing remote sensing methods and technologies using radiation from existing ground and space radio engineering systems. It is also valuable for students and educators in radiophysics, offering a comprehensive approach to AA design that could advance both academic study and practical applications in radio astronomy.