Signal Processing Networks Research Articles

Improved prediction of groundwater depth in urban areas using the EWT-S–G-GRU and LSSVM model: a case study of Xinxiang City

ABSTRACT The improvement in predicting groundwater depth accuracy has significant implications for the management, ecological environment protection, and economic and social development of regional water resources. Employing empirical wavelet transform (EWT) for nonlinear processing, Savitzky–Golay (S–G) filtering to reduce high-frequency noise, gate recurrent unit (GRU) neural network for linear feature signal processing, and least squares support vector machine (LSSVM) for nonlinear signal handling, we established a comprehensive model combining EWT-S–G-GRU and LSSVM. The results demonstrate that the proposed model exhibits superior accuracy in groundwater depth prediction, with an average relative error of only 2.14% and Nash–Sutcliffe efficiency (NSE) of 0.93. This outperforms the other four models, with average relative errors of 12.88%, 11.90%, 7.07%, and 11.10%, and NSE values of 0.58, 0.56, 0.71, and 0.73, respectively. The superiority of the model established in this study is attributed to its effective handling of both nonlinear and linear features of groundwater, thereby enhancing predictive accuracy. The EWT-S–G-GRU and LSSVM model proves to be more reliable in revealing the spatial distribution and dynamic changes of future groundwater, providing a robust reference for the rational development and utilization of groundwater in the urban area of Xinxiang City.

Retracted: Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

Retracted: Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

Phenotyping, genetics, and "-omics" approaches to unravel and introgress enhanced resistance against apple scab (Venturia inaequalis) in apple cultivars (Malus × domestica).

Apple scab disease, caused by the fungus Venturia inaequalis, endangers commercial apple production globally. It is predominantly managed by frequent fungicide sprays that can harm the environment and promote the development of fungicide-resistant strains. Cultivation of scab-resistant cultivars harboring diverse qualitative Rvi resistance loci and quantitative trait loci associated with scab resistance could reduce the chemical footprint. A comprehensive understanding of the host-pathogen interaction is, however, needed to efficiently breed cultivars with enhanced resistance against a variety of pathogenic strains. Breeding efforts should not only encompass pyramiding of Rvi loci and their corresponding resistance alleles that directly or indirectly recognize pathogen effectors, but should also integrate genes that contribute to effective downstream defense mechanisms. This review provides an overview of the phenotypic and genetic aspects of apple scab resistance, and currently known corresponding defense mechanisms. Implementation of recent "-omics" approaches has provided insights into the complex network of physiological, molecular, and signaling processes that occur before and upon scab infection, thereby revealing the importance of both constitutive and induced defense mechanisms. Based on the current knowledge, we outline advances toward more efficient introgression of enhanced scab resistance into novel apple cultivars by conventional breeding or genetic modification techniques. However, additional studies integrating different "-omics" approaches combined with functional studies will be necessary to unravel effective defense mechanisms as well as key regulatory genes underpinning scab resistance in apple. This crucial information will set the stage for successful knowledge-based breeding for enhanced scab resistance.

Ultra-Low Power Analog Folded Neural Network for Cardiovascular Health Monitoring.

Wearable sensors are increasingly used for continuous health monitoring, but their small size limits battery capacity, affecting user experience and monitoring capabilities. To overcome this, we introduce an ultra-low power analog Folded Neural Network (FNN) for physiological signal processing in a batteryless fashion. Our proposed FNN, by serializing computation, provides several benefits over traditional analog implementations, such as lower space, lower power consumption, and lower peak-to-average power ratio. We evaluate our method extensively using a dataset designed for ECG-based screening and diagnosis. Our analysis considers factors such as thermal noise, spatial requirements, and power consumption. Additionally, we evaluate detection performance, investigating various parameters of the proposed FNN. This evaluation provides insights into the optimal configuration for accurate anomaly detection. We observe a good trade-off for accuracy around 6 layers and a hidden size of 30 and further demonstrate that such architecture could be implemented in a wearable device and executed in a batteryless fashion.

Signal to Image Conversion and Convolutional Neural Networks for Physiological Signal Processing: A Review

Signal to Image Conversion and Convolutional Neural Networks for Physiological Signal Processing: A Review

Integrating Graph Signal Processing and Multitask Temporal Convolutional Networks for Household Nonintrusive Load Monitoring

Integrating Graph Signal Processing and Multitask Temporal Convolutional Networks for Household Nonintrusive Load Monitoring

Fast Cycling Speed with Multimillion Cycling Endurance of Ultra‐Low Loss Phase Change Material (Sb2Se3) by Engineered Laser Pulse Irradiation

AbstractProgrammable and reconfigurable photonics is revolutionized the next generation of Si/SiN‐based photonics processors, optical signal processing, and neural and quantum networks. Ultra‐low loss phase‐change technology has the enormous potential to offer energy‐efficient, extra large‐scale integrated (ELSI) nonvolatile programmable photonics that can overcome the limitations of commonly used volatile and energy‐hungry programmable photonic systems. So far, most of the phase change materials (PCM) devices have already shown to be suitable for programmable and reconfigurable photonic circuit applications that do not require extreme endurance or ultra‐fast switching speeds. However, applications with ultra‐fast speed and multi‐million cycling requirements, such as memory cells, quantum computing, optical displays, and optical modulators necessitate enormous improvements in the existing cycling speed and endurance of PCMs, for high‐performance device configuration. Here, optical switching of an ultra‐low loss PCM (Sb2Se3) is demonstrated with an unprecedented combination of rapid cycling speed (0.6 × 105 cycles s−1) and extreme endurance (> 2 × 106 cycles) that pave the way for ultrafast, large‐scale, programmable, and reconfigurable integrated photonic circuits and devices.

Neural Network Signal Processing of a Quasi-Distributed Fiber-Optic Measuring Network with Amplitude Sensors

Neural Network Signal Processing of a Quasi-Distributed Fiber-Optic Measuring Network with Amplitude Sensors

A large-scale integrated vector–matrix multiplication processor based on monolayer molybdenum disulfide memories

Data-driven algorithms—such as signal processing and artificial neural networks—are required to process and extract meaningful information from the massive amounts of data currently being produced in the world. This processing is, however, limited by the traditional von Neumann architecture with its physical separation of processing and memory, which motivates the development of in-memory computing. Here we report an integrated 32 × 32 vector–matrix multiplier with 1,024 floating-gate field-effect transistors that use monolayer molybdenum disulfide as the channel material. In our wafer-scale fabrication process, we achieve a high yield and low device-to-device variability, which are prerequisites for practical applications. A statistical analysis highlights the potential for multilevel and analogue storage with a single programming pulse, allowing our accelerator to be programmed using an efficient open-loop programming scheme. We also demonstrate reliable, discrete signal processing in a parallel manner.

VHXLA: A post-earthquake damage prediction method for high-speed railway track-bridge system using VMD and hybrid neural network

The timely and accurate prediction of post-earthquake damage is critically important for ensuring the safety of high-speed railway track-bridge systems. The study introduces a novel method known as the VHXLA model. This innovative model blends variational mode decomposition (VMD) with a hybrid neural network for predicting non-linear multi-component post-earthquake damage in high-speed railways. The VHXLA model comprises a signal processing module and a deep learning module. The signal processing module utilises VMD decomposition, and the Hilbert transform (HT) to transform two-dimensional seismic signals into four-dimensional complex time–frequency signals, thus simplifying the task of identifying seismic time–frequency characteristics via the neural network. The deep learning module integrates diverse types of neural network components, such as the Xception CNN feature extraction submodule, the LSTM RNN temporal learning submodule, and the multi-head attention mechanism submodule. A Bayesian self-optimisation method is implemented to determine the number of decomposition layers in VMD and select essential hyperparameters. The model's effectiveness is evaluated by predicting the damage of an experimentally validated finite element model, subjecting it to seismic loads, and thus gauging its performance. The results indicated that the signal processing module, based on VMD decomposition, significantly improves the neural network's signal processing capability. In addition, the synergistic integration of different modules in the VHXLA model provides superior prediction accuracy compared to pre-existing damage prediction models. Notably, the prediction accuracy is consistent across different positions of the same predicted component in the high-speed railway track-bridge system.

Physics-informed neural network assisted spherical microphone array signal processing

Thanks to their rotational symmetry that facilitates three-dimensional signal processing, spherical microphone arrays are the common array apertures used for spatial audio and acoustic applications. However, practical implementations of spherical microphone arrays suffer from two issues. First, at high frequency range, a large number of sensors are needed to accurately capture a sound field. Second, the accompanying signal processing algorithm, i.e., the spherical harmonic decomposition method, requires a variable radius array or a rigid surface array to circumvent the spherical Bessel function nulls. Such arrays are hard to design and introduce a scattering field. To address these issues, this paper proposes to assist a spherical microphone array with a physics-informed neural network (PINN) for three-dimensional signal processing. The PINN models the sound field around the array based on the sensor measurements and the acoustic wave equation, augmenting the sound field information captured by the array through prediction. This makes it possible to analyze a high frequency sound field with a reduced number of sensors and avoid the spherical Bessel function nulls with a simple single radius open-sphere microphone array.

New aspects of weaving K-frames: the excess and duality

Weaving frames in separable Hilbert spaces have been recently introduced by Bemrose et al. to deal with some problems in distributed signal processing and wireless sensor networks. Likewise weaving K -frames have been proved to be useful during signal reconstructions from the range of a bounded linear operator K. In this paper, we study the notion of weaving and its connection to the duality of K-frames and construct several pairs of woven K-frames. Also, we find a unique biorthogonal sequence for every K-Riesz basis and obtain a $K^*$-frame which is woven by its canonical dual. Moreover, we describe the excess for K-frames and prove that any two woven K-frames in a separable Hilbert space have the same excess. Finally, we introduce the necessary and sufficient condition under which a K-frame and its image under an invertible operator have the same excess.

Damage localization and quantification in offshore jacket structures using signal processing and intelligent system

Offshore platforms operate in harsh environmental conditions, and a significant portion of their structure is submerged underwater. Consequently, inspection and repair operations are costly and complex, making it crucial to have a precise damage detection system to increase safety and reduce maintenance expenses. This paper presents an intelligent signal processing-based system for damage detection in offshore jacket platforms. First, two damage indicators based on the signal's entropy and power are introduced to localize damage in jacket-type offshore structures. Then, an intelligent system that combines an artificial neural network and several signal processing techniques is used to quantify damage in various parts of these structures. To this end, various signal processing techniques extract the response signal features. Next, the proposed neural network model is trained using a data set, including the extracted feature vectors and the severity of the known damage scenarios. As a case study, an offshore jacket platform installed in the Persian Gulf was examined to evaluate the proposed method's effectiveness. The results reveal that the provided method detects the damage locations and severities with high processing speed and accuracy. Further, selecting suitable features and performing initial processing on training feature vectors can increase the accuracy of detecting damage intensity.

On-chip non-uniformly spaced multi-channel microwave photonic signal processor based on an ultrahigh-Q multimode micro-disk resonator.

Multi-channel microwave photonic (MWP) signal processing can simultaneously perform different task operations on multiple signals carried by multiple wavelengths, which holds great potential for ultrafast signal processing and characterization in a wavelength-division-multiplexed (WDM) network. As emerging telecommunication services create more data, an elastic optical network, which has a flexible and non-uniform spectrum channel spacing, is an alternative architecture to meet the ever-increasing data transfer need. Here, for the multi-channel ultra-fast signal processing in the elastic optical network, we propose and demonstrate an on-chip non-uniformly spaced multi-channel microwave photonic signal processor based on an ultrahigh-Q multimode micro-disk resonator (MDR). In the proposed signal processor, an MDR supporting multiple different order whispering-gallery modes (WGMs) with an ultrahigh Q-factor is specifically designed. Benefiting from the large and different free spectral ranges (FSRs) provided by the different order WGMs, a non-uniformly spaced multi-channel microwave photonic signal processor is realized, and various processing functions are experimentally demonstrated including bandpass filtering with a narrow passband of 103 MHz, a rejection ratio of 22.3 dB and a frequency tuning range from 1 to 30 GHz, multiple frequency measurement with a frequency measurement range from 1 to 30 GHz, a frequency resolution better than 200 MHz and a measurement accuracy of 91.3 MHz, and phase shifting with a phase tuning range from -170°∼160°, an operational bandwidth of 26 GHz from 6 GHz to 32 GHz and a small power variation of 0.43 dB. Thanks to the coexistence of different order WGMs supported by the MDR, non-uniformly spaced multi-channel signal processing is enabled with the key advantages including a broad operation bandwidth, an ultra-narrow frequency selectivity, and a large phase tuning range with a small power variation. The proposed signal processor is promising to be widely used in future elastic optical networks with flexible spectrum grids.

An SNN-Based Bionic Olfactory Signal Processing Network for Odor Recognition

Feature extraction in electronic nose (E-nose) signals is the first signal processing step in machine olfactory systems. Traditional feature extraction methods are often cumbersome and depend greatly on the experience of the researcher. The failure of feature extraction seriously deteriorates the performance of subsequent pattern recognition algorithms. In addition, inherent sensor signal shifts among sensing systems with identical configurations affect E-nose performance. Therefore, due to the unfavorable performance of empirical feature extraction and poor reproducibility, the odor detection ability of E-nose systems has deteriorated. In this paper, a bionic olfactory signal processing (BOSP) network based on a spiking neural network (SNN) is proposed to address these issues. Inspired by the structure of the olfactory bulb in mammals, the system has two components: a virtual olfactory receptor (VOR) layer and a bionic olfactory bulb (BOB) layer. The VORs convert the sensor response signals into spike sequences through two spike coding strategies. The BOBs, consisting of mitral cells and granule cells, extract spatiotemporal features from the spike sequences. The proposed BOSP network is a feature-level model that automatically extracts useful features from the sensor array outputs without applying traditional and tedious feature extraction steps, which can subsequently cascade various classifiers. The results of extensive experiments demonstrate that the BOSP network not only shows better feature extraction capability than several state-of-the-art methods but also exhibits promising shift suppression ability.

Topological network transport in on-chip phononic crystals

Rapid developments for topological materials have promoted the search for topological transport in the fields of condensed-matter physics and materials science. However, topological network transport, proposed in twisted bilayer graphene and soon after being explored in other two-dimensional materials, is still elusive despite extensive experimental efforts. Here, we implemented on-chip phononic crystal networks based on a network unit cell with six channels consisting of triangular prisms on a silicon substrate arranged in a honeycomb array. The topological network bulk transport in the gap of the bulk states of the original phononic crystal and the network edge transport in the gap of the network bulk states were visualized directly. These results offer a controllable platform for exploring topological transport in networks and may enable the realization of on-chip microultrasonic devices, such as splitters, filters, and signal processing in a monolithic elastic network.

Development of an intelligent dual-beam gamma densitometer for real-time recognition of two-phase flow regime in horizontal pipe

This work includes the hardware and software development of a high sampling rate dual-beam gamma densitometer for real-time two-phase flow monitoring. The designed densitometer consists of two fast SiPM-based gamma detectors and a custom embedded system for digital signal processing and running flow regime recognition neural network. An exclusive LSTM deep neural network has been developed to classify flow regimes into six classes including stratified, annular, bubbly, wavy, slug, and plug. Therefore, the proposed design is superior to the traditional approaches by (i), unifying signal processing and flow identification units into a single cost-effective embedded system; (ii), reducing the need for time-consuming signal preprocessing steps from conventional to feed-forward neural networks, and (iii), real-time identification of the flow due to the high refresh rate of the data processing chain. The optimal neural network model has been obtained through comprehensive sensitivity analyses to have the best interpolation and generalization. The experimental evaluations showed that the optimized model recognizes the flow regime based on 6 s of gamma data sequence with an accuracy of ∼92%–97% depending on the flow regime in the pipeline. Furthermore, the real-time experiment proved the successful performance of the densitometer to trace the flow regime across the two-phase flow map.

Fully Ferroelectric-FETs Reservoir Computing Network for Temporal and Random Signal Processing

Reservoir computing (RC), a derivation of recurrent neural networks (RNNs), is an energy-efficient computational framework suitable for temporal signal processing. Owing to the short-term and long-term memory capability, the ferroelectric field-effect transistor (FeFET) is regarded as a promising hardware component for implementing RC networks. This article aims to optimize the fully FeFETs RC network by evaluating the recognition accuracy in various classification tasks, which includes the operating voltage sequence, device numbers as well as connection methods. The physical random telegraph noise (RTN), working as an ideal temporal and random signal, is investigated and extended by using the optimized fully FeFET RC network, resulting in a rapid time constant extraction method. Our findings may provide the broad potential for hardware security and cyber security based on the fully FeFET RC network.

Preface

This issue of the Journal of Physics: Conference Series contains the proceedings of the 2022 2nd International Conference on Communication Technology and Information Technology (ICCTIT 2022). The conference took place in Harbin, China during December 16th-18th, 2022 (virtual conference), with the attendance of about 50 prominent delegates in related fields all around the world.The conference established an international academic forum for scholars, specialists and researchers in related fields to present their research activities, share their professional experiences, exchange new ideas, and discuss the key challenges and research directions facing the development of related fields, as well as promoted the development of communication technology and information technology.To make this conference on schedule, we had a three-part plenary conference including a keynote speech part delivered by four speakers from different parts of China. Among them, Assoc. Prof. Pingyi Fan from Tsinghua University, China performed a keynote speech on Timeliness Challenges for 6G Wireless Networks Toward Metaverse. In this speech, he mainly focused on the implementation timeliness. If the promising scenarios is coming, what techniques we need to prepare in 6G wireless networks. He first introduced the Meatverse concept and its potential applications in our daily life and then present its challenge requirements for communication technologies, especially, on the implementation timeliness. Later on, he analyzed the existing status for wireless networks from multiple layers and point out the possible solutions to the existing problems and their corresponding new research directions. Finally, he gave a short summary to show what he and his team need to do in next step. We are very optimistic that all participants will acquire new ideas and knowledge from this conference to enhance the capability toward communication technology and information technology.Through a rigorous peer-review process, all submissions were performed double blind review to check their quality of content, level of innovation, significance, originality and legibility. Based on the expertise review comments, some excellent papers were accepted and the corresponding authors were invited to submit their papers for final publication. The accepted papers included topics on: Network and Wireless Communication, Network Information Security, Coding and Signal Processing, Data Science and Systems, Web Technologies, etc.We would like to thank the Conference Chairman and the Technical Program Committees for their hard work that lead to the organization of this event, to our colleagues who contributed to its realization, to the members of the Journal of Physics: Conference Series for their patience while waiting for the final versions of the papers and to every author who has contributed with an article to these proceedings. We firmly believe that all the attendees have had fruitful discussions and gained valuable knowledge, and will enjoy the opportunity for future collaborations.The Committee of ICCTIT 2022List of Committee member is available in this Pdf.

A novel transfer learning network with adaptive input length selection and lightweight structure for bearing fault diagnosis

In recent years, great progress has been made in intelligent bearing fault diagnosis based on transfer learning (TL). However, the huge number of parameters is ignored when using large convolutional neural network (CNN), and the input length of different bearings are almost not take into account. The high-energy hardware economic cost and time consumption caused by slow operation of large CNN have brought great difficulties to the engineering practice. Therefore, inspired by envelope demodulation and lightweight network signal processing methods, a novel lightweight TL network is proposed, which can adaptively select the input length (IL) and accurately identify the bearing health states under different work conditions. Firstly, an innovative adaptive IL selection strategy considering bearing differences is proposed to replace manually fixed IL. Secondly, a TL network containing group convolution and instance normalization is constructed to make the network lightweight and operate faster. Thirdly, maximum mean discrepancy is introduced to align the feature distribution between source domain and target domain. Lastly, 81 tasks are carried out on the across-domain datasets to validate the practicability of the proposed method. The results between accuracy and lightweight demonstrate that the proposed method is superior to other four state-of-the-art TL CNN, including three TL CNN and a lightweight model, under identical conditions.