Acoustic Recognition Research Articles

AI-driven Wildlife Behavior Monitoring using Computer Vision

Abstract- As human-wildlife interaction grows more frequent and wildlife habitats face increasing environmental pressures, monitoring animal behavior has become crucial for conservation efforts and ecological research. This paper presents an AI-driven Wildlife Behavior Monitoring System using computer vision, deep learning, and YOLOv8 to detect, classify, and analyze wildlife activities in real-time. The proposed system accurately identifies species and tracks behaviors such as feeding, movement, resting, and social interactions across diverse habitats. It provides detailed insights through spatial and temporal mapping, revealing patterns like migration routes and seasonal behavioral changes. Advanced anomaly detection flags unusual behaviors, such as distress or potential poaching, triggering alerts for conservationists. The system’s dashboard visualizes live animal detection, historical data, and behavior reports, assisting researchers in studying long-term behavioral trends. Future features include predictive analytics for forecasting wildlife behavior, edge AI for remote monitoring, and acoustic recognition to monitor elusive species. By offering real-time monitoring and data-driven insights, this AI-powered system aims to revolutionize wildlife research and conservation, ensuring proactive protection and sustainable wildlife management.

An End-to-End Underwater Acoustic Target Recognition Model Based on One-Dimensional Convolution and Transformer

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition.

An open-set recognition method for ship radiated noise signal based on graph convolutional neural network prototype learning

The underwater acoustic perception system often undertakes multi-class ship recognition tasks. As the underwater acoustic recognition environment becomes increasingly complex, underwater acoustic perception systems often face various interference, such as unknown ships radiated noise signals and decoy signals. This poses higher requirements on the robustness and recognition capabilities of underwater acoustic target recognition methods. In this paper, we transform the problem of underwater acoustic target recognition in this scenario into an open-set recognition problem. We design a deep learning model based on graph convolutional neural networks, propose a graph embedding method for time-domain ships radiated noise signals, and propose a fully parameterized prototype learning framework. We simulate decoy signals in real sea areas, and all the data used in the experiments are actual collected data. The fully parameterized prototype learning framework based on a data-driven approach can not only effectively resist interference from unknown ship-radiated noise signals and decoy signals, but also accurately identify multi-class target ship-radiated noise signals. Ultimately, our method achieves end-to-end open-set recognition of ship-radiated noise signals.

Snn and sound: a comprehensive review of spiking neural networks in sound.

The rapid advancement of AI and machine learning has significantly enhanced sound and acoustic recognition technologies, moving beyond traditional models to more sophisticated neural network-based methods. Among these, Spiking Neural Networks (SNNs) are particularly noteworthy. SNNs mimic biological neurons and operate on principles similar to the human brain, using analog computing mechanisms. This capability allows for efficient sound processing with low power consumption and minimal latency, ideal for real-time applications in embedded systems. This paper reviews recent developments in SNNs for sound recognition, underscoring their potential to overcome the limitations of digital computing and suggesting directions for future research. The unique attributes of SNNs could lead to breakthroughs in mimicking human auditory processing more closely.

Acoustic recognition of predators by mantled howler monkeys (Alouatta palliata): A playback experiment with naïve and experienced subjects.

When the production of antipredator behaviors is costly, prey is expected to stop displaying such behaviors and lose the ability to recognize extirpated predators. However, the loss or maintenance of predator recognition abilities is conditional on the eco-evolutionary context of prey. Here, we examined the behavioral responses of naïve and experienced mantled howler monkeys (Alouatta palliata) to simulated acoustic cues from natural predators. We studied experienced individuals in the Uxpanapa Valley and naïve individuals in Los Tuxtlas (Veracruz, México). Jaguars (Panthera onca) and harpy eagles (Harpia harpyja), the main predators of howler monkeys, are extant in the Uxpanapa Valley but have been extirpated in Los Tuxtlas for approximately 70 and 45 years, respectively. We exposed six naïve and six experienced groups to playbacks of acoustic stimuli from the two predators and a non-predator control species (plain chachalacas, Ortalis vetula), and recorded the latency, frequency, and duration of antipredation behaviors (n = 127 trials). In contrast with experienced mantled howler monkeys, naïve subjects did not respond to trials from harpy eagles. However, response patterns were generally similar between naïve and experienced individuals when exposed to jaguar stimuli. Our findings suggest that naïve mantled howler monkeys do not recognize harpy eagle calls, but they respond to jaguar calls in a manner consistent with experienced individuals. These results illustrate how different mechanisms for the recognition of extirpated predators operate within a single species according to evolutionary and ecological experience.

Feature Extraction Methods for Underwater Acoustic Target Recognition of Divers.

The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency-domain multi-sub-band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal-to-noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets.

Advancing robust underwater acoustic target recognition through multitask learning and multi-gate mixture of experts.

Underwater acoustic target recognition has emerged as a prominent research area within the field of underwater acoustics. However, the current availability of authentic underwater acoustic signal recordings remains limited, which hinders data-driven acoustic recognition models from learning robust patterns of targets from a limited set of intricate underwater signals, thereby compromising their stability in practical applications. To overcome these limitations, this study proposes a recognition framework called M3 (multitask, multi-gate, multi-expert) to enhance the model's ability to capture robust patterns by making it aware of the inherent properties of targets. In this framework, an auxiliary task that focuses on target properties, such as estimating target size, is designed. The auxiliary task then shares parameters with the recognition task to realize multitask learning. This paradigm allows the model to concentrate on shared information across tasks and identify robust patterns of targets in a regularized manner, thus, enhancing the model's generalization ability. Moreover, M3 incorporates multi-expert and multi-gate mechanisms, allowing for the allocation of distinct parameter spaces to various underwater signals. This enables the model to process intricate signal patterns in a fine-grained and differentiated manner. To evaluate the effectiveness of M3, extensive experiments were implemented on the ShipsEar underwater ship-radiated noise dataset. The results substantiate that M3 has the ability to outperform the most advanced single-task recognition models, thereby achieving the state-of-the-art performance.

Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation.

The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analyzing human disturbance and for privacy filtering. Despite significant strides in deep learning in recent years, the deployment of large neural networks on compact devices poses challenges due to memory and latency constraints. Our approach focuses on leveraging knowledge distillation techniques to design efficient, lightweight student models for speech detection in bioacoustics. In particular, we employed the MobileNetV3-Small-Pi model to create compact yet effective student architectures to compare against the larger EcoVAD teacher model, a well-regarded voice detection architecture in eco-acoustic monitoring. The comparative analysis included examining various configurations of the MobileNetV3-Small-Pi-derived student models to identify optimal performance. Additionally, a thorough evaluation of different distillation techniques was conducted to ascertain the most effective method for model selection. Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring.

Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: A phylogenetic perspective

The study of bird populations is crucial for biodiversity research and conservation. Deep artificial neural networks have revolutionized bird acoustic recognition; however, most methods overlook inherent relationships among bird populations, resulting in the loss of biological information. To address this limitation, we propose the Phylogenetic Perspective Neural Network (PPNN), which incorporates hierarchical multilevel labels for each bird. PPNN uses a hierarchical semantic embedding framework to capture feature information at different levels. Attention mechanisms are employed to extract and select common and distinguishing features, thereby improving classification accuracy. We also propose a path correction strategy to rectify inconsistent predictions. Experimental results on bird acoustic datasets demonstrate that PPNN outperforms current methods, achieving classification accuracies of 90.450%, 91.883%, and 89.950% on the Lishui-Zhejiang Birdsdata (100 species), BirdCLEF2018-Small (150 species), and BirdCLEF2018-Large (500 species) datasets respectively, with the lowest hierarchical distance of a mistake across all datasets. Our proposed method is applicable to any bird acoustic dataset and presents significant advantages as the number of categories increases.

AR-AI assisted ophthalmic nursing: Preliminary usability study in clinical settings.

Ophthalmic ward nursing work is onerous and busy, and many researchers have tried to introduce artificial intelligence (AI) technology to assist nurses in performing nursing tasks. This study aims to use augmented reality (AR) and AI technology to develop an intelligent assistant system for ophthalmic ward nurses and evaluate the usability and acceptability of the system in assisting clinical work for nurses. Based on AR technology, under the framework of deep learning, the system management, functions, and interfaces were completed using acoustic recognition, voice interaction, and image recognition technologies. Finally, an intelligent assistance system with functions such as patient face recognition, automatic information matching, and nursing work management was developed. Ophthalmic day ward nurses were invited to participate in filling out the System Usability Scale (SUS). Using the AR-based intelligent assistance system (AR-IAS) as the experimental group and the existing personal digital assistant (PDA) system as the control group. The experimental results of the three subscales of learnability, efficiency, and satisfaction of the usability scale were compared, and the clinical usability score of the AR-IAS system was calculated. This study showed that the AR-IAS and the PDA systems had learnability subscale scores of 22.50/30.00 and 21.00/30.00, respectively; efficiency subscale scores of 29.67/40.00 and 28.67/40.00, respectively; and satisfaction subscale scores of 23.67/30.00 and 23.17/30.00, respectively. The overall usability score of the AR-IAS system was 75.83/100.00. Based on the analysis results of the System Usability Scale, the AR-IAS system developed using AR and AI technology has good overall usability and can be accepted by clinical nurses. It is suitable for use in ophthalmic nursing tasks and has clinical promotion and further research value.

New stakes and challenges for transport noise observatories, Case studies for Acoucité, skills cluster for French Metropolises

Founded in 1996, at the initiative of research centers and Metropolises, Acoucité is the first independent structure, a non-profit association, entirely dedicated to the observation of transport noise. It supports a dozen noise observatories based on: - Acoustic measurement networks; - Noise maps complying with European standards; - Perception surveys. This article presents the new regulatory, technological and ethical challenges that noise observatories must meet, and in particular those related to: - New technologies and their articulation with regulations on the protection of personal data (when using artificial intelligence for acoustic recognition from recordings); - Work on noise cameras in order to fine abnormally noisy vehicles; - New indicators characterizing event noise, which will soon enter the regulations; - Regulations (LOM) that define noise not as an annoyance but as a pollution; - Health issues of noise (as a pollution) through the health impacts documented by the WHO (guideline for Europe, 2018); - Cross-consideration of environmental pollution (noise and air quality). Observatories must indeed contribute to a better knowledge and understanding of these emerging issues by relying on the scientific community.

Construction of English Speech Recognition Model by Fusing CNN and Random Deep Factorization TDNN

In current society, speech recognition can perform a variety of functions, such as completing voice commands, enabling speech processing, spoken language translation and facilitating communication. Therefore, the study of speech recognition technology is of high value. However, current speech recognition techniques focus on among clearly expressed spoken words, which poses great challenges for recognition with spoken pronunciation or dialect pronunciation. Some scholars currently use a model combining time-delay neural networks and long and short-term memory networks to build speech recognition systems, but the performance in acoustic recognition is poor. Therefore, the study proposes a convolutional neural network (CNN), time-delay neural network (TDNN) and output-gate projected Gated recurrent by analyzing the deep neural network unit (OPGRU) combined with a composite English speech recognition model. The model can optimize the acoustic model after the introduction of CNN, and the model can accurately recognize pronunciation features and make the model have a wider recognition range. The proposed composite model is compared with the Word error rate (Wer) and runtime metrics in the Mozilla Common Voice dataset. The Wer result of the composite model is 23.42% and the running time is 1418 s. The Wer result of the composite model is 24.61% and the running time is 1385 s. Compared with the TDNN-OPGRU model, the Wer of the composite model decreases by 1.19% but the running time increases by 33 s. The accuracy of the composite model is higher than that of the TDNN-OPGRU model. From a comprehensive consideration, the speech recognition model accuracy has higher priority, so the composite model proposed in the study has better performance.

Detection of invasive fish species with passive acoustics: Discriminating between native and non-indigenous sciaenids

Invasive alien species have been rising exponentially in the last decades impacting biodiversity and ecosystem functioning. The soniferous weakfish, Cynoscion regalis, is a recent invasive sciaenid species in the Iberian Peninsula and was first reported in the Tagus estuary in 2015. There is concern about its possible impacts on native species, namely the confamiliar meagre, Argyrosomus regius, as there is overlap in their feeding regime, habitat use, and breeding behaviour. Here, we characterised the sciaenid-like sounds recently recorded in the Tagus estuary and showed that they are made by weakfish as they have similar numbers of pulses and pulse periods to the sounds made by captive breeding weakfish. We further demonstrate that breeding grunts from weakfish and the native sciaenid, recorded either in captivity or Tagus estuary, differ markedly in sound duration, number of pulses and pulse period in the two species, but overlap in their spectral features. Importantly, these differences are easily detected through visual and aural inspections of the recordings, making acoustic recognition easy even for the non-trained person. We propose that passive acoustic monitoring can be a cost-effective tool for in situ mapping of weakfish outside its natural distribution and an invaluable tool for early detection and to monitor its expansion.

The Most Effective Method to Recognize and Assess Bugs in Food Grains

Abstract: Food grains that are stored are significantly damaged by insects. Additionally, the nutritional and marketability of stored food grains are impacted by insect pestilence. The application of corrective measures necessitates the prompt identification and surveillance of insects in the stored food grains. Insect traps, Berlese funnels, visual lures, pheromone devices, and other methods of visual inspection are a few of the most common approaches that are extensively utilized in commercial grain storage facilities or granaries. Of late, electronic nose, strong stage miniature extraction, warm imaging, acoustic recognition, and so on. have been shown to be effective at identifying insects. The most important aspects to take into account when choosing a method are its capacity for in-situ early detection, monitoring, cost, dependability, and labor requirements. An important concern for reducing losses in bulk storage warehouses is the detection of hidden infestations, whose population may be many times greater than that of free-living insects. This allows for prompt fumigation or grain disposal. This paper surveys a portion of the broadly involved location techniques for early recognition of bugs' pestilential exercises in put away food grains as well as a portion of the clever advances with an accentuation on acoustic strategy, which has a decent business potential.

Automatic acoustic recognition of pollinating bee species can be highly improved by Deep Learning models accompanied by pre-training and strong data augmentation.

Bees capable of performing floral sonication (or buzz-pollination) are among the most effective pollinators of blueberries. However, the quality of pollination provided varies greatly among species visiting the flowers. Consequently, the correct identification of flower visitors becomes indispensable to distinguishing the most efficient pollinators of blueberry. However, taxonomic identification normally depends on microscopic characteristics and the active participation of experts in the decision-making process. Moreover, the many species of bees (20,507 worldwide) and other insects are a challenge for a decreasing number of insect taxonomists. To overcome the limitations of traditional taxonomy, automatic classification systems of insects based on Machine-Learning (ML) have been raised for detecting and distinguishing a wide variety of bioacoustic signals, including bee buzzing sounds. Despite that, classical ML algorithms fed by spectrogram-type data only reached marginal performance for bee ID recognition. On the other hand, emerging systems from Deep Learning (DL), especially Convolutional Neural Networks (CNNs), have provided a substantial boost to classification performance in other audio domains, but have yet to be tested for acoustic bee species recognition tasks. Therefore, we aimed to automatically identify blueberry pollinating bee species based on characteristics of their buzzing sounds using DL algorithms. We designed CNN models combined with Log Mel-Spectrogram representations and strong data augmentation and compared their performance at recognizing blueberry pollinating bee species with the current state-of-the-art models for automatic recognition of bee species. We found that CNN models performed better at assigning bee buzzing sounds to their respective taxa than expected by chance. However, CNN models were highly dependent on acoustic data pre-training and data augmentation to outperform classical ML classifiers in recognizing bee buzzing sounds. Under these conditions, the CNN models could lead to automating the taxonomic recognition of flower-visiting bees of blueberry crops. However, there is still room to improve the performance of CNN models by focusing on recording samples for poorly represented bee species. Automatic acoustic recognition associated with the degree of efficiency of a bee species to pollinate a particular crop would result in a comprehensive and powerful tool for recognizing those that best pollinate and increase fruit yields.

A Fine-Grained Ship-Radiated Noise Recognition System Using Deep Hybrid Neural Networks with Multi-Scale Features

Fine-grained ship-radiated noise recognition methods of different specific ships are in demand for maritime traffic safety and general security. Due to the high background noise and complex transmission channels in the marine environment, the accurate identification of ship radiation noise becomes quite complicated. Existing ship-radiated noise-based recognition systems still have some shortcomings, such as the imperfection of ship-radiated noise feature extraction and recognition algorithms, which lead to distinguishing only the type of ships rather than identifying the specific vessel. To address these issues, we propose a fine-grained ship-radiated noise recognition system that utilizes multi-scale features from the amplitude–frequency–time domain and incorporates a multi-scale feature adaptive generalized network (MFAGNet). In the feature extraction process, to cope with highly non-stationary and non-linear noise signals, the improved Hilbert–Huang transform algorithm applies the permutation entropy-based signal decomposition to perform effective decomposition analysis. Subsequently, six learnable amplitude–time–frequency features are extracted by using six-order decomposed signals, which contain more comprehensive information on the original ship-radiated noise. In the recognition process, MFAGNet is designed by applying unique combinations of one-dimensional convolutional neural networks (1D CNN) and long short-term memory (LSTM) networks. This architecture obtains regional high-level information and aggregate temporal characteristics to enhance the capability to focus on time–frequency information. The experimental results show that MFAGNet is better than other baseline methods and achieves a total accuracy of 98.89% in recognizing 12 different specific noises from ShipsEar. Additionally, other datasets are utilized to validate the universality of the method, which achieves the classification accuracy of 98.90% in four common types of ships. Therefore, the proposed method can efficiently and accurately extract the features of ship-radiated noises. These results suggest that our proposed method, as a novel underwater acoustic recognition technology, is effective for different underwater acoustic signals.

Research on Acoustic Recognition of Underwater Targets based on the Principle of Sound Absorption

Due to the concealment of underwater engineering, the detection and monitoring of hydraulic structure construction is often more complicated and difficult than surface engineering. Cofferdam construction inspection and settlement construction quality inspection, especially to ensure the accuracy and efficiency of inspection in complex environments, have become an urgent problem to be solved in underwater engineering quality inspection and monitoring. This project designs an offshore fixed-point monitoring device based on wave energy and piezoelectric effect energy. The invention provides a wave power generation system. The system include simple structure, reliability and stability The fixed-point monitoring device combines the cantilever float device, the gas storage tank device and the piezoelectric ceramic energy supply device to provide the marine monitoring device with energy for automatic water surface monitoring, fixed-point and data collection. Finally, the comprehensive utilization of sea surface wave, compressed gas energy storage and piezoelectric effect energy is realized.

Underwater Acoustic Target Recognition Based on Data Augmentation and Residual CNN

In the field of underwater acoustic recognition, machine learning methods rely on a large number of datasets to achieve high accuracy, while the actual collected signal samples are often very scarce, which has a great impact on the recognition performance. This paper presents a recognition method of an underwater acoustic target by the data augmentation technique and the residual convolutional neural network (CNN) model, which is used to expand training samples to improve recognition performance. As a representative model in residual CNN, the ResNet18 model is used for recognition. The whole process mainly includes mel-frequency cepstral coefficient (MFCC) feature extraction, data augmentation processing, and ResNet18 model recognition. On the base of the traditional data augmentation, this study used the deep convolutional generative adversarial network (DCGAN) model to realize the expansion of underwater acoustic samples and compared the recognition performance of support vector machine (SVM), common CNN, VGG19, and ResNet18. The recognition results of the MFCC, constant Q transform (CQT), and low-frequency analyzer and recorder (LOFAR) spectrum were also analyzed and compared. Experimental results showed that the recognition accuracy of the MFCC feature was better than that of other features at the same method, and using the data augmentation method could obviously improve the recognition performance. Moreover, the recognition performance of ResNet18 using data enhancement technology was better than that of other models, which was due to the combination of the data expansion advantage of data augmentation technology and the deep feature extracting ability of the residual CNN model. In addition, although this method was used for ship recognition in this paper, it is not limited to this. This method is also applicable to other target voice recognition, such as natural sound and underwater voice biometrics.

A Low-Power Hardware Architecture for Real-Time CNN Computing.

Convolutional neural network (CNN) is widely deployed on edge devices, performing tasks such as objective detection, image recognition and acoustic recognition. However, the limited resources and strict power constraints of edge devices pose a great challenge to applying the computationally intensive CNN models. In addition, for the edge applications with real-time requirements, such as real-time computing (RTC) systems, the computations need to be completed considering the required timing constraint, so it is more difficult to trade off between computational latency and power consumption. In this paper, we propose a low-power CNN accelerator for edge inference of RTC systems, where the computations are operated in a column-wise manner, to realize an immediate computation for the currently available input data. We observe that most computations of some CNN kernels in deep layers can be completed in multiple cycles, while not affecting the overall computational latency. Thus, we present a multi-cycle scheme to conduct the column-wise convolutional operations to reduce the hardware resource and power consumption. We present hardware architecture for the multi-cycle scheme as a domain-specific CNN architecture, which is then implemented in a 65 nm technology. We prove our proposed approach realizes up to 8.45%, 49.41% and 50.64% power reductions for LeNet, AlexNet and VGG16, respectively. The experimental results show that our approach tends to cause a larger power reduction for the CNN models with greater depth, larger kernels and more channels.

Acoustic Wake-Up Technology for Microsystems: A Review.

Microsystems with capabilities of acoustic signal perception and recognition are widely used in unattended monitoring applications. In order to realize long-term and large-scale monitoring, microsystems with ultra-low power consumption are always required. Acoustic wake-up is one of the solutions to effectively reduce the power consumption of microsystems, especially for monitoring sparse events. This paper presents a review of acoustic wake-up technologies for microsystems. Acoustic sensing, acoustic recognition, and system working mode switching are the basis for constructing acoustic wake-up microsystems. First, state-of-the-art MEMS acoustic transducers suitable for acoustic wake-up microsystems are investigated, including MEMS microphones, MEMS hydrophones, and MEMS acoustic switches. Acoustic transducers with low power consumption, high sensitivity, low noise, and small size are attributes needed by the acoustic wake-up microsystem. Next, acoustic features and acoustic classification algorithms for target and event recognition are studied and summarized. More acoustic features and more computation are generally required to achieve better recognition performance while consuming more power. After that, four different system wake-up architectures are summarized. Acoustic wake-up microsystems with absolutely zero power consumption in sleep mode can be realized in the architecture of zero-power recognition and zero-power sleep. Applications of acoustic wake-up microsystems are then elaborated, which are closely related to scientific research and our daily life. Finally, challenges and future research directions of acoustic wake-up microsystems are elaborated. With breakthroughs in software and hardware technologies, acoustic wake-up microsystems can be deployed for ultra-long-term and ultra-large-scale use in various fields, and play important roles in the Internet of Things.