Computational Complexity Research Articles

Direction of Arrival Estimation Based on DNN and CNN

The accuracy of Direction of Arrival (DOA) estimation primarily depends on the precision of the data. When the receiver uses a low-precision analog-to-digital converter (ADC), traditional DOA estimation algorithms exhibit poor accuracy. To face the challenge of multi-target DOA estimation in scenarios with low-precision ADC quantized sampling, this paper proposes a novel DOA estimation algorithm for quantized signals based on classification problems. A deep learning network was constructed using Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), divided into the quantized signal recovery framework and the DOA estimation framework. The DNN network is utilized to recover signals that have undergone low-precision quantization, while the CNN network addresses the classification problem to estimate the DOA from received data with an unknown number of signal sources. A comprehensive analysis of the impact of signal-to-noise ratio (SNR), the number of array elements, and the number of quantization bits on the proposed algorithm was conducted. Simulation results indicate that the proposed algorithm exhibits superior DOA estimation performance in low-precision scenarios, characterized by reduced computational complexity, thereby facilitating real-time DOA estimation.

High-Performance Grape Disease Detection Method Using Multimodal Data and Parallel Activation Functions

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model’s computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection.

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

Revocable and Fog-Enabled Proxy Re-Encryption Scheme for IoT Environments

As technology advances rapidly, a diverse array of Internet of Things (IoT) devices finds widespread application across numerous fields. The intelligent nature of these devices not only gives people more convenience, but also introduces new challenges especially in security when transmitting data in fog-based cloud environments. In fog computing environments, data need to be transmitted across multiple devices, increasing the risk of data being intercepted or tampered with during transmission. To securely share cloud ciphertexts, an alleged proxy re-encryption approach is a commonly adopted solution. Without decrypting the original ciphertext, such a mechanism permits a ciphertext intended for user A to be easily converted into the one intended for user B. However, to revoke the decryption privilege of data users usually relies on the system authority to maintain a user revocation list which inevitably increases the storage space. In this research, the authors come up with a fog-based proxy re-encryption system with revocable identity. Without maintaining the traditional user revocation list, the proposed scheme introduces a time-updated key mechanism. The time-update key could be viewed as a partial private key and should be renewed with different time periods. A revoked user is unable to obtain the renewed time-update key and hence cannot share or decrypt cloud ciphertexts. We formally demonstrate that the introduced scheme satisfies the security of indistinguishability against adaptively chosen identity and chosen plaintext attacks (IND-PrID-CPA) assuming the hardness of the Decisional Bilinear Diffie–Hellman (DBDH) problem in the random oracle model. Furthermore, compared with similar systems, the proposed one also has lower computational complexity as a whole.

Two-dimensional hyperchaos-based encryption and compression algorithm for agricultural UAV-captured planar images.

This study presents an approach that integrates compressed sensing technology with two-dimensional hyperchaotic coupled Fourier oscillator systems (2D-HCFOS) to address the challenge of slow encryption speeds in agricultural unmanned aerial vehicles (UAVs). The primary challenge in enhancing encryption speed lies in the limited capacity inherent in traditional chaotic-based systems and the computational complexity of their processes. The 2D-HCFOS utilizes a complex two-dimensional hybrid chaotic system, which significantly enhances the security of agricultural UAV image data. Notably, the image encryption process is performed on a personal computer connected to the drone, ensuring efficient processing. By integrating advanced Fourier series and nonlinear coupled oscillators, the model surpasses existing chaotic-based methods, improving both the pseudo-randomness and robustness of encryption. Additionally, incorporating Bonouille functions into the discrete cosine transform (DCT) domain results in a sparser measurement matrix, which is essential for efficient encryption on personal computers. The effectiveness of 2D-HCFOS in securely encrypting agricultural drone images has been rigorously validated through simulations and analytical evaluations using sophisticated row, rotation, and matrix encryption techniques. The improved security performance is further verified by comparative analysis. Compared with other models, the Lyapunov index of 2D-HCFOS is 15.1039, and the sample entropy is 2.4987, indicating that it possesses superior chaotic performance and encryption reliability.

Quantized kernel recursive q-Rényi-like algorithm

This paper introduces the kernel recursive q-Rényi-like (KRqRL) algorithm, based on the q-Rényi kernel function and the kernel recursive least squares (KRLS) algorithm. To reduce the computational complexity and memory requirements of the KRqRL algorithm, an online vector quantization (VQ) method is employed to quantize the network size to a codebook size, resulting in the quantized KRqRL (QKRqRL) algorithm. This paper provides a detailed analysis of the convergence and computational complexity of the QKRqRL algorithm. In the simulation experiments, the network size of each algorithm is reduced to 25% of its original size. The performance of the QKRqRL algorithm is evaluated in terms of convergence speed, prediction error, and computation time under non-Gaussian noise conditions. Finally, the QKRqRL algorithm is further validated using sunspot data, demonstrating its superior stability and online prediction performance.

A novel rehabilitation action recognition approach using AF-LiteFormer from millimeter wave radar

Abstract Rehabilitation action recognition is a hot research topic in the medical field, which serves as the foundation for achieving remote healthcare, unsupervised exercise, intelligent home healthcare, and possesses extensive application value. Currently, vision-based action recognition methods are susceptible to limitations imposed by factors such as range of motion and environmental lighting during human motion capture. Due to its ability to effectively protect patient privacy and its immunity to lighting conditions, this paper proposed a millimeter-wave radar-based rehabilitation action recognition system, AF-LiteFormer. Firstly, EfficientFormerV2 is employed as the baseline, then a Lite-MSLA-FFN block-and-layer is designed to replace MHSA in EfficientFormerV2, which improves the diversity of attention and reduces computational complexity. Meanwhile, a Lite-Subsample block-and-layer is designed to replace the dual-path downsampling part that composed of attention downsampling and stride attention to realize global modeling and multi-scale learning.
Secondly, the iterative attention feature fusion (iAFF) mechanism is introduced to improve the recognition accuracy of rehabilitation actions. Finally, the effectiveness of the AF-LiteFormer model is validated on a self-collected rehabilitation action dataset and a publicly available micro-Doppler dataset. Experimental results show that the overall performance of the AF-LiteFormer model is better than the State-of-the-Art model (SOTA), the recognition accuracy of rehabilitation actions is as high as 99.7%, and it has strong generalization ability.

The computational complexity of finding stationary points in non-convex optimization

The computational complexity of finding stationary points in non-convex optimization

LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification

Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.

SparseMorph: A weakly-supervised lightweight sparse transformer for mono- and multi-modal deformable image registration

PurposeDeformable image registration (DIR) is crucial for improving the precision of clinical diagnosis. Recent Transformer-based DIR methods have shown promising performance by capturing long-range dependencies. Nevertheless, these methods still grapple with high computational complexity. This work aims to enhance the performance of DIR in both computational efficiency and registration accuracy. MethodsWe proposed a weakly-supervised lightweight Transformer model, named SparseMorph. To reduce computational complexity without compromising the representative feature capture ability, we designed a sparse multi-head self-attention (SMHA) mechanism. To accumulate representative features while preserving high computational efficiency, we constructed a multi-branch multi-layer perception (MMLP) module. Additionally, we developed an anatomically-constrained weakly-supervised strategy to guide the alignment of regions-of-interest in mono- and multi-modal images. ResultsWe assessed SparseMorph in terms of registration accuracy and computational complexity.Within the mono-modal brain datasets IXI and OASIS, our SparseMorph outperforms the state-of-the-art method TransMatch with improvements of 3.2 % and 2.9 % in DSC scores for MRI-to-CT registration tasks, respectively. Moreover, in the multi-modal cardiac dataset MMWHS, our SparseMorph shows DSC score improvements of 9.7 % and 11.4 % compared to TransMatch in MRI-to-CT and CT-to-MRI registration tasks, respectively. Notably, SparseMorph attains these performance advantages while utilizing 33.33 % of the parameters of TransMatch. ConclusionsThe proposed weakly-supervised deformable image registration model, SparseMorph, demonstrates efficiency in both mono- and multi-modal registration tasks, exhibiting superior performance compared to state-of-the-art algorithms, and establishing an effective DIR method for clinical applications.

Intrinsic Universality in Automata Networks III: On Symmetry versus Asynchrony

An automata network is a finite assembly of interconnected entities endowed with a set of local maps defined over a common finite alphabet. These relationships are represented through an interaction graph. Together with the local functions, an assignment known as an update scheme directs the evolution of the network by updating specific subsets of entities at discrete time steps. Despite the scrutiny of interaction graphs and update schemes, their profound impact on automata network dynamics remains largely open. This work investigates the intricate interplay between these aspects, with a focus on how update schemes can counterbalance constraints stemming from symmetric local interactions. This paper is the third of a series about intrinsic universality, a notion that assesses both dynamical and computational complexity, encompassing transient behaviors, attractors, and prediction or reachability problems. We consider four update modes—parallel, block-sequential, local clocks, and general periodic— along with several families of symmetric signed conjunctive boolean networks defined by local constraints on signs. Our main result is to show a diagonal complexity leap in this two-dimensional landscape: the stronger the local constraints the higher the level of asynchrony required to obtain intrinsic universality or increase in complexity. We also show how in some cases asynchronism allows to simulate directed interactions from undirected ones with the same local rules.

Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images

Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate bounding box overlap. This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency. Furthermore, to enhance real-world feasibility, we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing: frame splitting and threading. In addition, we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms, such as drones. This work contributes to the field of earthquake damage detection by (1) demonstrating the effectiveness of deep learning models, including adapted architectures, for damage detection from drone imagery, (2) highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements, and (3) proposing methods for ensemble model processing and model optimization to enhance real-world feasibility. The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation, rescue efforts, and recovery operations in the aftermath of earthquakes.

An outcome space algorithm for solving general linear multiplicative programming

The following article presents and corroborates an outcome space branch-and-bound algorithm for solving the general linear multiplicative programming problem (GLMPP). In this new algorithm, GLMPP is transformed into its equivalent problem by introducing auxiliary variables. To compute the tight upper bound for the optimal value of GLMPP, the linear relaxation programming problem is assembled by using bound and hull linear approximations. Furthermore, the global convergence and computational complexity of the algorithm are demonstrated. Finally, the soundness and advantage of the proposed algorithm are validated by solving a demonstrative linear multiplicative problem.

Saliency and location aware pruning of deep visual detectors for autonomous driving

Despite the remarkable achievements of deep neural networks, their high computational complexity limits their wide use in many real-world embedded applications, such as autonomous driving perception. While current neural network pruning approaches can reduce model complexity to various extents, they often adopt local or ad hoc importance measures that are not directly related to the final task. More importantly, most of them focus on classification tasks and do not take location information into consideration during pruning. To address these issues, we present a novel channel importance measure that incorporates detection-related saliency and location awareness, specifically designed for the pruning of self-driving visual detectors. Our comprehensive experiments on the KITTI and COCO_traffic datasets demonstrate that our pruning method achieves significant reductions in model size and computational operations with little performance degradation. Notably, it outperforms other state-of-the-art methods across various pruning rates and base detectors. Our pruned YOLOX-S model with 40.2% fewer parameters even improves the original model’s mAP by 1.8% on KITTI. Moreover, we experimentally highlight the potential of our pruning approach in effectively detecting small-scale objects.

A Highly Efficient Compressive Sensing Algorithm Based on Root-Sparse Bayesian Learning for RFPA Radar

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during the fine search stage. Meanwhile, to ensure computational feasibility, the proposed algorithm utilizes a simple single-level hierarchical prior distribution model and employs the derived root-solving formula to avoid the refinement of velocity grids. Moreover, during the fine search stage, the proposed algorithm combines the fixed-point strategy with the Expectation-Maximization algorithm to update the hyperparameters, further reducing computational complexity. In terms of implementation, the proposed algorithm updates hyperparameters based on the single-level prior distribution to approximate values for the range and velocity parameters during the coarse search stage. Subsequently, in the fine search stage, the proposed algorithm performs a grid search only in the range dimension and uses the derived root-solving formula to directly solve for the target velocity parameters. Simulation results demonstrate that the proposed algorithm maintains low computational complexity while exhibiting stable performance for parameter estimation in various multi-target off-grid scenarios.

Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches.

An Improved Unfolded Coprime Linear Array Design for DOA Estimation with No Phase Ambiguity

In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of source angles. To solve the phase ambiguity problem, we reconstruct an improved unfolded coprime linear array (IUCLA) by rearranging the reference element of the prototype UCLA. Specifically, we design the multiple coprime inter pairs by introducing the third coprime integer, which can be pairwise with the other two integers. Then, the phase ambiguity problem can be solved via the multiple coprime property. Furthermore, we employ a spectral peak searching method that can exploit the whole aperture and full DOFs of the IUCLA to detect targets and achieve angle estimation. Meanwhile, the proposed method avoids extra processing in eliminating ambiguous angles, and reduces the computational complexity. Finally, the Cramer–Rao bound (CRB) and numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed method.

Ethereum blockchain for electronic health records: securing and streamlining patient management.

Electronic health records (EHRs) are increasingly replacing traditional paper-based medical records due to their speed, security, and ability to eliminate redundant data. However, challenges such as EHR interoperability and privacy concerns remain unresolved. Blockchain, a distributed ledger technology comprising connected, encrypted data blocks, presents a promising solution. This study explores how blockchain technology can revolutionize hospital EHR management. Our proposed solution securely transfers medical records between patients and doctors using the InterPlanetary File System (IPFS) and the Ethereum platform. Utilizing smart contracts automates data transfers, ensuring patient anonymity and reducing computational complexity while securely storing patient data on the network. Patient records are stored locally on the Ganache server, with the front end managed using HTML, CSS, ReactJS, and JavaScript, and the backend developed in Solidity. Blockchain technologies combined with Role- Based access control instead of attribute -based access control. The system's throughput increases linearly with the number of users and requests, enhancing the framework's efficiency and scalability. The minimum recorded latency is 14 ms.

Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study

In the last few decades, major progress has been made in the medical field; in particular, new treatments and advanced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life. As a consequence, the number of elderly people is expected to increase in the following years. This trend, along with the need to improve the independence of frail people, has led to the development of unobtrusive solutions to monitor daily activities and provide feedback in case of risky situations and falls. Monitoring devices based on radar sensors represent a possible approach to tackle postural analysis while preserving the person’s privacy and are especially useful in domestic environments. This work presents an innovative solution that combines millimeter-wave radar technology with artificial intelligence (AI) to detect different types of postures: a series of algorithms and neural network methodologies are evaluated using experimental acquisitions with healthy subjects. All methods produce very good results according to the main parameters evaluating performance; the long short-term memory (LSTM) and GRU show the most consistent results while, at the same time, maintaining reduced computational complexity, thus providing a very good candidate to be implemented in a dedicated embedded system designed to monitor postures.

Jet Tagging with More-Interaction Particle Transformer

Abstract In this study, we introduce the More-Interaction Particle Transformer (MIParT), a novel deep learning neural network designed for jet tagging. This framework incorporates our own design, the More-Interaction Attention (MIA) mechanism, which increases the dimensionality of particle interaction embeddings. We tested MIParT using the top tagging and quark-gluon datasets. Our results show that MIParT not only matches the accuracy and AUC of LorentzNet and a series of Lorentz-equivariant methods, but also significantly outperforms the ParT model in background rejection. Specifically, it improves background rejection by approximately 25\% at a 30\% signal efficiency on the top tagging dataset and by 3\% on the quark-gluon dataset. Additionally, MIParT requires only 30\% of the parameters and 53\% of the computational complexity needed by ParT, proving that high performance can be achieved with reduced model complexity. For very large datasets, we double the dimension of particle embeddings, referring to this variant as MIParT-Large (MIParT-L). We find that MIParT-L can further capitalize on the knowledge from large datasets. From a model pre-trained on the 100M JetClass dataset, the background rejection performance of the fine-tuned MIParT-L improved by 39\% on the top tagging dataset and by 6\% on the quark-gluon dataset, surpassing that of the fine-tuned ParT. Specifically, the background rejection of fine-tuned MIParT-L improved by an additional 2\% compared to the fine-tuned ParT. The results suggest that MIParT has the potential to advance efficiency benchmarks for jet tagging and event identification in particle physics.Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd