Signal Processing Community Research Articles

Resonance scattering generated by rotating bodies

Abstract The scattering of rotating bodies to a polarized plane wave, including the dielectric cylinder and sphere, is studied. The resonance caused by rotation is emphasized. Numerical results prove that the resonance scattering caused by rotation can be realized in the optical range. It is sensitive to the rotation dimensionless parameter γ. The internal Mie mode corresponding to the electromagnetic field intensity changes with γ, and the resonant mode appears when the particle rotates at a specific speed. Moreover, the resonant mode changes with γ. It causes resonance scattering to appear in the same particle at different speeds. Inside particles, resonant rings are composed of a series of array points and are determined by γ. Under resonance conditions, the energy near the rotating cylinder is consistent with its rotation direction. In contrast, the direction of energy flow in the rotating sphere model is opposite to the direction of particle rotation. This work provides a novel idea for the design of ultra-sensitive sensors and resonators. It has promising applications in optical communication, optical microscopy, and optical signal processing.

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

Polarization-diverse soliton transitions and deterministic switching dynamics in strongly-coupled and self-stabilized microresonator frequency combs

Dissipative Kerr soliton microcombs in microresonators have enabled fundamental advances in chip-scale precision metrology, communication, spectroscopy, and parallel signal processing. Here we demonstrate polarization-diverse soliton transitions and deterministic switching dynamics of a self-stabilized microcomb in a strongly-coupled dispersion-managed microresonator driven with a single pump laser. The switching dynamics are induced by the differential thermorefractivity between coupled transverse-magnetic and transverse-electric supermodes during the forward-backward pump detunings. The achieved large soliton existence range and deterministic transitions benefit from the switching dynamics, leading to the cross-polarized soliton microcomb formation when driven in the transverse-magnetic supermode of the single resonator. Secondly, we demonstrate two distinct polarization-diverse soliton formation routes – arising from chaotic or periodically-modulated waveforms via pump power selection. Thirdly, to observe the cross-polarized supermode transition dynamics, we develop a parametric temporal magnifier with picosecond resolution, MHz frame rate and sub-ns temporal windows. We construct picosecond temporal transition portraits in 100-ns recording length of the strongly-coupled solitons, mapping the transitions from multiple soliton molecular states to singlet solitons. This study underpins polarization-diverse soliton microcombs for chip-scale ultrashort pulse generation, supporting applications in frequency and precision metrology, communications, spectroscopy and information processing.

AMSVT: audio Mel-spectrogram vision transformer for spoken Arabic digit recognition

This work presents a novel model to recognize spoken digits in the Arabic language. Due to the transformer-based models' tremendous success in natural language processing (NLP), several attempts have been made to extend transformer-based designs to other domains, such as vision and audio. However, our approach consists of extracting and inputting Mel-spectrogram features into our model of the proposed audio Mel-spectrogram vision transformer (AMSVT) for training. The signal processing community has been interested in these models due to the successful use of vision transformers (ViT) in several computer vision applications. This is because signals are frequently recorded as spectrograms (using the Mel-spectrogram, for example), which may be given directly as input to vision transformers. Our model outperformed a group of models in terms of accuracy and time, such as convolutional neural network (CNN)-based and recurrent neural network (RNN)-based.

Acoustic notch filter based on self-collimation sonic crystals

Utilizing the self-collimation effect in two-dimensional (2D) sonic crystals (SCs), we have proposed an acoustic notch filter which is composed of two perfect mirrors and a beam splitter. Numerical simulation shows that by properly designing the inside structure of the 2D SC, the self-collimated acoustic waves with a desired frequency can be trapped between the two mirrors, realizing the notch filtering function. Moreover, the frequency and bandwidth of the notch filter can be arbitrarily adjusted by changing the parameters of the internel structure, in a considerably broad band. Potential applications include acoustic communication and acoustic signal processing, especially for underwater circumstances.

Application of deep learning-based speech signal processing technology in electronic communication

In recent years, the artificial intelligence boom triggered by deep learning is influencing and changing peoples lifestyles. People are no longer satisfied with human-computer interaction through simple text commands; instead, they look forward to more convenient and faster communication methods like voice interaction. Against the backdrop of innovative development, the application of speech signal processing systems is becoming increasingly widespread. Therefore, it is necessary to study the application of deep learning-based speech signal processing technology in electronic communication. This can provide more valuable references and assistance for future development, promoting the better development of deep learning-based speech signal processing technology in electronic communication. In this paper, we first review the application of deep learning in speech signal enhancement, speech recognition, and speech synthesis from a theoretical analysis perspective. Then, we discuss the application of deep learning-based speech signal processing in electronic communication, including the application of models such as Transformer, LAS (Listen, Attend and Spell), and GFT-conformer in speech signal processing. We also discuss some application scenarios of deep learning-based speech signal processing in electronic communication. Finally, we identify the need for deeper application of deep learning technology in speech signal processing and electronic communication, with continuous optimization and adjustment.

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer.

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods' intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods.

Macaque claustrum, pulvinar and putative dorsolateral amygdala support the cross-modal association of social audio-visual stimuli based on meaning.

Social communication draws on several cognitive functions such as perception, emotion recognition and attention. The association of audio-visual information is essential to the processing of species-specific communication signals. In this study, we use functional magnetic resonance imaging in order to identify the subcortical areas involved in the cross-modal association of visual and auditory information based on their common social meaning. We identified three subcortical regions involved in audio-visual processing of species-specific communicative signals: the dorsolateral amygdala, the claustrum and the pulvinar. These regions responded to visual, auditory congruent and audio-visual stimulations. However, none of them was significantly activated when the auditory stimuli were semantically incongruent with the visual context, thus showing an influence of visual context on auditory processing. For example, positive vocalization (coos) activated the three subcortical regions when presented in the context of positive facial expression (lipsmacks) but not when presented in the context of negative facial expression (aggressive faces). In addition, the medial pulvinar and the amygdala presented multisensory integration such that audiovisual stimuli resulted in activations that were significantly higher than those observed for the highest unimodal response. Last, the pulvinar responded in a task-dependent manner, along a specific spatial sensory gradient. We propose that the dorsolateral amygdala, the claustrum and the pulvinar belong to a multisensory network that modulates the perception of visual socioemotional information and vocalizations as a function of the relevance of the stimuli in the social context. SIGNIFICANCE STATEMENT: Understanding and correctly associating socioemotional information across sensory modalities, such that happy faces predict laughter and escape scenes predict screams, is essential when living in complex social groups. With the use of functional magnetic imaging in the awake macaque, we identify three subcortical structures-dorsolateral amygdala, claustrum and pulvinar-that only respond to auditory information that matches the ongoing visual socioemotional context, such as hearing positively valenced coo calls and seeing positively valenced mutual grooming monkeys. We additionally describe task-dependent activations in the pulvinar, organizing along a specific spatial sensory gradient, supporting its role as a network regulator.

Wigner distribution associated with linear canonical transform of generalized 2-D analytic signals

Analytic complex signals find numerous applications in image and signal processing community. This article focuses on the discussion and definitions of three generalized two-dimensional (2-D) analytic signals (GASs). It begins by explaining the parametric Hilbert transform (PHT) and parametric Riesz transform (PRT), derived from analyzing the irrotational and solenoidal vector fields, respectively. Furthermore, the article explores the four-dimensional Wigner distributions associated with the linear canonical transform (WDLs) for these GASs. Finally, the advantages of these GASs are demonstrated through their application in envelope detection, and highlighting these WDLs potential for feature extraction. This article provides valuable insights into the field of 2-D analytic signals and their applications in various domains.

A bias-correction modeling method of Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements

Modeling of Hammerstein–Wiener nonlinear systems has received a lot of attention in the signal processing community. However, all existing model identification methods may fail to provide a consistent parameter estimate for Errors-In-Variables (EIV) Hammerstein–Wiener systems, where both input–output data are contaminated by measurement white noises. In this paper, a bias-correction Least-Squares (LS) algorithm for consistent identification of EIV Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements is proposed. Firstly, the analytic expression for the estimated bias of the LS algorithm using noisy measurements for EIV Hammerstein–Wiener systems with polynomial nonlinearities is derived, which is caused by the correlation between the input–output signals and measurement noises. Secondly, a consistent estimation method for the bias-correcting term, including a recursive step and a cross-validation step based on the available noisy measurements only, is then proposed to estimate the unknown terms of noises variances and noise-free measurements in the estimated bias. The effectiveness of the proposed algorithm is demonstrated through a simulated example and a robot arm system.

Design and Simulation of variable gain amplifier using cadence Tool

The radio frequency (RF) amplifiers are widely used in a variety of communication systems. However, the conventional analog RF resulted in reduced volage gain, magnitude, and phase responses. So, this work provides an overview of a research paper focused on the design and analysis of a single-stage variable gain amplifier (SSVGA) utilizing cascaded linear transconductance amplifier (Gm cell) and linear transimpedance amplifier (TIA) blocks with feedback via shunt resistors. The SSVGA architecture aims to maintain constant bandwidth while offering controllable voltage gain, making it versatile for applications with varying input signal strengths. The first stage of the SSVGA is realized as a current mode TIA, converting the input voltage signal to an output current efficiently. The second stage features a Gm cell with source degeneration, enhancing bias current efficiency and transconductance at the supply voltage. The proposed SSVGA design offers flexibility and adaptability, making it suitable for diverse communication systems and signal processing applications. The incorporation of feedback control ensures consistent performance across different voltage gain settings, resulting in a robust and efficient solution for varying signal strengths.

Carrier Aggregation Enabled Integrated Sensing and Communication Signal Design and Processing

Carrier Aggregation Enabled Integrated Sensing and Communication Signal Design and Processing

Size matters: individual variation in auditory sensitivity may influence sexual selection in Pacific treefrogs (Pseudacris regilla).

The matched filter hypothesis proposes a close match between senders and receivers and is supported by several studies on variation in signal properties and sensory-processing mechanisms among species and populations. Importantly, within populations, individual variation in sensory processing may affect how receivers perceive signals. Our main goals were to characterize hearing sensitivity of Pacific treefrogs (Pseudacris regilla), assess patterns of individual variation in hearing sensitivity, and evaluate how among-individual variation in hearing sensitivity and call frequency content affect auditory processing of communication signals. Overall, males and females are most sensitive to frequencies between 2.0 and 2.5kHz, which matches the dominant frequency of the call, and have a second region of high sensitivity between 400 and 800Hz that does not match the fundamental frequency of the call. We found high levels of among-individual variation in hearing sensitivity, primarily driven by subject size. Importantly, patterns of among-individual variation in hearing differ between males and females. Cross-correlation analyses reveal that among-individual variation in hearing sensitivity may lead to differences on how receivers, particularly females, perceive male calls. Our results suggest that individual variation in sensory processing may affect signal perception and influence the evolution of sexually selected traits.

Design and implementation of intelligent‐oriented electronic communication power monitoring system

AbstractIn order to improve the monitoring effect of electronic communication power supply, this paper applies the intelligent communication signal processing method to the monitoring system, and deeply analyzes the basic principle of OTA. Moreover, from the perspective of improving linearity, a signal attenuation OTA is designed in this paper, and its linearity has been greatly improved. In addition, this paper designs a cross‐coupled POTA that can not only ensure high linearity but also achieve tunable translinearity. The tunable POTA circuit has simple structure, wide linear input range, tunable translinearity and easy implementation, and can meet its performance requirements in FPAA arrays. Through data analysis, it can be seen that the intelligent‐oriented electronic communication power monitoring system proposed in this paper has a good data monitoring effect. This study provides an effective approach for the development of electronic communication power monitoring technology, improves the signal processing efficiency of electronic communication power monitoring, and also provides some reference for the improvement of signal strength in electronic communication technology.

Generalized spectrum analysis of Chirp Cyclostationary signals associate with linear canonical transform

A chirp cyclostationary (CCS) process is a nonstationary stochastic process, whose statistics have been defined and utilized in radar and communication signal processing. However, spectrum analysis of CCS signals, which provides a spectral view to characterize a signal, has not been performed. Classical spectrum analysis is based on the Fourier transform, which is not suitable for CCS signals because of the multiplicative chirp signals in a CCS model. In this study, we propose a generalized spectral representation of chirp cyclostationary signals associated with a linear canonical transform. The proposed representation shows that a CCS signal can be represented by a linear combination of a series of jointly stationary and equal-bandwidth processes, which bridge the nonstationary and stationary processes. Furthermore, the relationships between the second-order statistics of the CCS process and its stationary representations are introduced. Finally, based on the above observations, a linear time-varying Wiener filter was designed for CCS signal denoising and time-varying channel estimation. Finally, the relationships between the nonstationarity of the input and output and time-varying properties of the systems are discussed, which are helpful for determining the nonstationarity of the outputs or the time-varying property of a system.

KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch.

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models.

BDCDTA-based Reconfigurable Frequency Selective Filter Structures for Medium Wave Communication and Biomedical Signal Processing Applications

BDCDTA-based Reconfigurable Frequency Selective Filter Structures for Medium Wave Communication and Biomedical Signal Processing Applications

Applied Nonlinear Analysis for Efficient Communication Signal Processing and Network Management

The use of nonlinear analysis in network management and communication signal processing is a revolutionary strategy that is redefining contemporary networking paradigms. The goal of this project is to improve communication systems' resilience, efficiency, and agility by integrating cutting-edge nonlinear techniques. This study investigates the complex relationship between signal processing and network management using nonlinear analysis techniques in order to meet the growing needs of modern networks. Nonlinear approaches provide dynamic signal processing methods, enabling in-the-moment modifications that maximise data transfer under variable network circumstances. Prioritising data streams is made easier by this integration, which also improves network speed and efficiently allocates resources to guarantee Quality of Service (QoS). The study explores the complexities of interdisciplinary ideas, using the concepts of nonlinear analysis to comprehend phase transitions, emergent network architectures, and networks' inherent ability to self-organize. By deciphering the complexity of network behaviours and their evolution, this understanding aids in the creation of more effective management techniques. However, there are difficulties with computing complexity, interoperability, and skill adaption when using nonlinear analysis in network management. To properly utilise nonlinear approaches for network management and communication signal processing optimisation, these obstacles must be overcome. The research is important because it may lead to game-changing advancements in communication systems by introducing cutting-edge ways that go beyond the constraints of conventional, linear methods. The findings show potential for more effective and secure communication systems, which can meet the increasing needs of modern networking.

Integration of Plasmonic Structures in Photonic Waveguides Enables Novel Electromagnetic Functionalities in Photonic Circuits

The development of integrated, compact, and multifunctional photonic circuits is crucial in increasing the capacity of all-optical signal processing for communications, data management, and microsystems. Plasmonics brings compactness to numerous photonic functions, but its integration into circuits is not straightforward due to insertion losses and poor mode matching. The purpose of this article is to detail the integration strategies of plasmonic structures on dielectric waveguides, and to show through some examples the variety and the application prospect of integrated plasmonic functions.