Low Time Complexity Research Articles

Research on the active cancellation technology of spectrum symbiotic interference

In this article, an active spectrum symbiotic interference (SSI) cancellation architecture is proposed for the spectrum symbiotic wireless communication systems. To minimize the residual reception power, a joint SSI cancellation algorithm based on time -amplitude -phase-three-dimensional search is proposed, which actively utilizes the characteristics of transmitted signals in the spectrum symbiotic wireless communication systems to adjust, reconstruct, and cancel the SSI from the aspects of time, amplitude, and phase. Theoretical and simulation analyses demonstrate that the proposed architecture and algorithm can suppress the SSI to the noise level, exhibiting lower space and time complexity.

A feature augmentation-based method for constructing generative-discriminative hybrid models

From the perspective of probability framework, the generative model first learns the joint probability distribution from the data and then calculates the conditional probability distribution with a faster convergence speed. However, the discriminative model learns the conditional probability distribution directly from the data, thus often demonstrating higher accuracy. As an effective combination of the generative and discriminative models, the generative-discriminative hybrid model integrates their advantages. However, the existing methods must divide the original features into two independent feature spaces to train the two models. Feature division not only increases the time complexity of the model but also weakens the expression ability of the original feature space. To solve this problem, this paper proposes a feature augmentation-based method for constructing the generative-discriminative hybrid model. First, this novel method uses the generative model to learn the conditional probability distribution. Then, it augments the learned conditional probability distribution as new features into the original feature space. Finally, it trains the discriminative model in the augmented feature space and predicts the final classification result. The new method offers several advantages, including using feature augmentation to mix the models, not requiring feature division, exhibiting low time complexity, and enhancing the expression ability of the original feature space. The experimental results on 36 classical UCI benchmark datasets show that the new method is not only effective and universal but also follows bias-variance trade-off.

A Chaotic Image Encryption Scheme Based on Genetic Central Dogma and KMP Method

In this paper, an image cryptosystem based on genetic central dogma (GCD), Knuth–Morria–Pratt (KMP) algorithm and a chaotic system is developed. The KMP algorithm is firstly used to bind DNA strings to obtain the next array, which participates in the design of the chaotic initial condition, and then the secure chaotic sequences are produced by employing the sliding idea in pattern string matching. In the present procedure, a DNA-level two-way pixel’s shuffle is achieved by a shared stack push operation and it is adopted in the permutation module for the purpose of accelerating the overall pixel’s shuffle. Subsequently, the pixel values are substituted by simulating the process of protein synthesis in GCD, in which the DNA replication and RNA replication form the basis of the DNA-triploid mutation and new RNA mutation rules, respectively. Experimental simulations and extensive cryptanalysis fully vindicate that superior security effects in addition to satisfactory low time complexity can be simultaneously obtained by the proposed confusion-substitution scheme.

A plain-image correlative semi-selective medical image encryption algorithm using enhanced 2D-logistic map.

Modern medical examinations have produced a large number of medical images. It is a great challenge to transmit and store them quickly and securely. Existing solutions mainly use medical image encryption algorithms, but these encryption algorithms, which were developed for ordinary images, are time-consuming and must cope with insufficient security considerations when encrypting medical images. Compared with ordinary images, medical images can be divided into the region of interest and the region of background. In this paper, based on this characteristic, a plain-image correlative semi-selective medical image encryption algorithm using the enhanced two dimensional Logistic map was proposed. First, the region of interest of a plain medical image is permuted at the pixel level, then for the whole medical image, substitution is performed pixel by pixel. An ideal compromise between encryption speed and security can be achieved by full-encrypting the region of interest and semi-encrypting the region of background. Several main types of medical images and some normal images were selected as the samples for simulation, and main image cryptanalysis methods were used to analyze the results. The results showed that the cipher-images have a good visual quality, high information entropy, low correlation between adjacent pixels, as well as uniformly distribute histogram. The algorithm is sensitive to the initial key and plain-image, and has a large keyspace and low time complexity. The time complexity is lower when compared with the current medical image full encryption algorithm, and the security performance is better when compared with the current medical image selective encryption algorithm.

Related-Key Differential Attacks on Reduced-Round LBlock

LBlock, as one of the typical lightweight encryption schemes, is a 32-round block cipher with 64 bit block and 80 bit master key. It can be widely applied in the IoT environment because of its friendly software and hardware implementations. Since it came out, it has encountered many attacks. In this paper, we evaluate LBlock’s ability against related-key differential attack more accurately based on SMT method. On the one hand, we propose tighter lower bounds on the minimal number of active S-boxes for up to 19 rounds of LBlock, which are 8 more rounds than previous ones. Then, we propose the upper bounds of total probabilities for up to 19 rounds of LBlock for the first time. On the other hand, with a suitable 17-round related-key differential distinguisher, we propose attacks on 22- and 23-round LBlock. Each of these attacks has lower time complexity and data complexity than previous ones for the same rounds of LBlock.

New Cryptanalysis of ZUC-256 Initialization Using Modular Differences

ZUC-256 is a stream cipher designed for 5G applications by the ZUC team. Together with AES-256 and SNOW-V, it is currently being under evaluation for standardized algorithms in 5G mobile telecommunications by Security Algorithms Group of Experts (SAGE). A notable feature of the round update function of ZUC-256 is that many operations are defined over different fields, which significantly increases the difficulty to analyze the algorithm.As a main contribution, with the tools of the modular difference, signed difference and XOR difference, we develop new techniques to carefully control the interactions between these operations defined over different fields. At first glance, our techniques are somewhat similar to those developed by Wang et al. for the MD-SHA hash family. However, as ZUC-256 is quite different from the MD-SHA hash family and its round function is much more complex, we are indeed dealing with different problems and overcoming new obstacles.As main results, by utilizing complex input differences, we can present the first distinguishing attacks on 31 out of 33 rounds of ZUC-256 and 30 out of 33 rounds of the new version of ZUC-256 called ZUC-256-v2 with low time and data complexities, respectively. These attacks target the initialization phase and work in the related-key model with weak keys. Moreover, with a novel IV-correcting technique, we show how to efficiently recover at least 16 key bits for 15-round ZUC-256 and 14-round ZUC-256-v2 in the related-key setting, respectively. It is unpredictable whether our attacks can be further extended to more rounds with more advanced techniques. Based on the current attacks, we believe that the full 33 initialization rounds provide marginal security.

Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach

Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging, especially when the numbers of IoT devices and UAVs are very large. In this paper, we formulate the joint optimization of UAV locations and relay paths in UAV-relayed IoT networks as a graph problem, and propose a graph neural network (GNN)-based approach to solve it in an efficient and scalable way. In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user. The theoretical analysis shows that the time complexity of RGNN is two orders lower than the conventional optimization method. Then, we jointly exploit location GNN (LGNN) and RGNN trained to optimize the locations of all UAVs. Both GNNs can be trained without relying on the training data, which is usually unavailable in the context of wireless networks. In inference procedure, LGNN is first used to optimize the location of UAVs, and then RGNN is used to select the best relay path based on the output of LGNN. Simulation results show that the proposed approach can achieve comparable performance to brute-force search with much lower time complexity when the network is relatively small. Remarkably, the proposed approach is highly scalable to large-scale networks and adaptable to dynamics in the environment, which can hardly be achieved using conventional methods.

Convolutional Neural Network Enable Optoelectronic System for Predicting Cardiac Response by Analyzing Auction-Based Optimization Algorithms

One of the body’s most important organs is the heart. An electrocardiogram (ECG) is a common diagnostic tool because it provides continuous tracings of the heart’s electrophysiological activity. The study’s overarching objective is the development and implementation of an artificial intelligence (AI)-based abnormal heart beat detection system with potential applications in the early diagnosis and timely treatment of cardiovascular diseases. Through the transmission of signals to the healthcare monitoring system, these wearable devices enable doctors to keep constant, reliable tabs on their patients’ health statuses. In addition to alerting the doctors and nurses, this serves as a warning to the patient so that they, too, can take preventative measures. Several scientific teams utilizing AI contributed to the victory. Predicting cardiovascular disease using information gathered from smart devices is challenging due to low accuracy and time complexity. We propose a new optimization strategy based on deep learning to tackle these problems. In particular, it relies on the Condition-Convolutional Neural Network (Condition-CNN) based Auction-based Optimization algorithm, which analyzes optimization algorithms (ABO) while also considering Opto electronics property (sensor and detector characteristics, MOSFET) mechanism details, and the active element triumvirate.

Minor motion magnification based on spectral analysis

Objects in the world are constantly in motion, and it is crucial for us to observe how they change. Although our visual system possesses significant levels of perception and interpretation of motion, it has limited sensitivity, the observer cannot perceive minor motions which are below the visual sensing threshold. Minor motion cannot readily be perceived via conventional methods. In this paper, a Euler motion amplification method based on the back projection of the derivative is proposed, in which the Riesz pyramid decomposition is applied for achieving a better hierarchical reconstruction. Compared to more traditional methods, the method proposed herein can hold much larger magnification, has lower time complexity, and is more resistant to noise. Experiments have demonstrated that the given algorithm achieves better minor magnification with the evaluation of the objective quantitative evaluation indicators.

Users' experience matter: Delay sensitivity-aware computation offloading in mobile edge computing

As a promising computing paradigm, Mobile Edge Computing (MEC) provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements, constrained battery capacity and limited bandwidth of the Internet of Things (IoT) systems. Most existing works on mobile edge task ignores the delay sensitivities, which may lead to the degraded utility of computation offloading and dissatisfied users. In this paper, we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints. Specifically, we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories. Then, we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading (CIRO) algorithm to collect all information in the MEC system. By starting with an initial offloading strategy, the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges. Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.

Satellite Edge Computing Architecture and Network Slice Scheduling for IoT Support

For 5G and 6G communications, satellites are drawing great attention for global coverage extension and 3-D mobility enhancement. With advancements of satellite hardware, functional satellites are expected to be applied for 6G Internet of Things (IoT) services. In particular, because IoT service has a relatively low computational burden, it is more feasible for satellite edge computing (SatEC) with limited power, making IoT supportable SatEC one of the economically feasible applications for future satellite networks. In this article, an architecture of IoT supportable SatEC is analyzed, and the corresponding network slice scheduling is proposed. First, a multiobjective optimization problem for IoT supportable SatEC is formulated with respect to latency, computational power, and transmission power attenuation. The problem is solved for the satellite offloading rate and altitude by using a heuristic algorithm in low time complexity with time-varying satellite constellation topology and various service requirements for simulations. Next, to analyze the expandability of the SatEC IoT network, a sliced SatEC IoT scheduling problem is formulated in the normalized weighted sum of latency, computational power, and transmission power attenuation. Scheduling rules are proposed to prioritize various applications with the results of the scheduling problem and with the proper SatEC offloading rates predefined in the Pareto optimality of the satellite edge multiobjective Tabu search (SE-MOTS). Finally, efficient satellite constellations are determined by comparing low-Earth orbit (LEO) and very LEO (VLEO) satellite networks, in terms of proper satellite altitudes and offloading strategies for IoT supportable SatEC. Based on simulation results, a scheduling rule for sliced satellite network and a proper offloading strategy of different slices are proposed, and an appropriate altitude of the satellite network for sliced SatEC is discussed.

Broad fuzzy cognitive map systems for time series classification

Time series classification (TSC) is a crucial and challenging problem in sequential analysis. However, most of the existing best-performing methods are time-consuming, even if coping with small-scale datasets. Broad learning systems (BLS) have shown low time complexity and high accuracy in handling various tasks and have been applied to many fields. However, none of the BLS-based methods is suitable to tackle TSC due to (1) unsuitable structure in capturing temporal information of time series; (2) low interpretability in representing the evolving relations among states along time. Thus, this paper develops a broad fuzzy cognitive map system (BFCMS) to address time series classification efficiently, which consists of the sparse autoencoder (SAE) based feature extraction block, the high-order fuzzy cognitive map (HFCM) based spatiotemporal information aggregation block, and one multilayer perceptron (MLP) based prediction layer. The feature extraction block is designed to capture the underlying core evolving patterns, and the spatiotemporal information aggregation block is developed to model the underlying causal relationships and contextual dependencies. These two blocks are designed to overcome the limitations of BLS. MLP is applied to map the feature representation to the label of time series based on the aggregated feature representations from these two blocks. In addition, BFCMS develops three incremental learning strategies for fast updating in broad expansion without a retraining procedure if the model deems to be expanded. We compared BFCMS with other state-of-the-art baselines on 26 datasets. The experimental results demonstrate the superiority of BFCMS. Concretely, BFCMS achieves a lower training cost with on-par classification accuracy.

Continual learning fault diagnosis method based on grid-based artificial immune system

In recent years, continual learning for class increments has attracted a great deal of attention. The ontinual-learning classification method (CLCM based on an artificial immune system (AIS) can identify unknown faults during testing. However, the CLCM still has the problem of excessive runtime consumption. Therefore, it is crucial to improve the efficiency of the immune algorithm and take advantage of its continual learning mechanism in the field of fault diagnosis. In this paper, a continual learning fault diagnosis method based on sparse grid and the AIS, which called sparse grid classification method (SGCM), is proposed, which is inspired by grid-based techniques and the CLCM based on an AIS. Firstly, a new cell generation strategy is proposed to reduce the time complexity and improve the diagnosis efficiency; therefore, the problem of dimension explosion is avoided. In addition, the memory cell coding capabilities of the SGCM increases the utilization rate of cells so as to simplify the calculation of affinity. At the same time, the conceived cell backtracking strategy enhances the continual learning ability of the algorithm so that new fault types can be quickly identified through the existing learning results. Ultimately, the model adaptive adjustment method inspired by a single-layer feed-forward neural network improves the generalization power and the accuracy of classification. We conduct experiments on well-known datasets from the UCI repository to assess the performance of the SGCM. To evaluate the fault diagnosis performance of the SGCM, experiments on a reciprocating compressor experimental dataset and the XJTU-SY rolling element bearing dataset were performed. The results show that theSGCM is a fast fault diagnosis method with low time complexity and continual learning ability.

A two-step optimization approach for structures investigation of Pd-Ir bimetallic nanoclusters

The structures of nanoalloys are crucial for understanding their catalytic performance because the stable structures can affect the activity and selectivity of nanocatalysts. Therefore, investigating the stable structures of nanoalloys is important to understand their catalytic activities. In this article, a two-step optimization approach by combining a basin hoping genetic algorithm (BHGA) and DFT calculation is employed to investigate the structural stability of Pd-Ir alloy nanoclusters. Different compositions (Pd and Ir atoms proportion) and different initial configurations of 38-atom Pd-Ir alloys are considered. The combination of BHGA and DFT method can effectively find the stable structures of truncated octahedral (TO) configurations for 38-atom Pd-Ir alloys with high accuracy and low time complexity. Furthermore, the formation energy and bond energy of Pd-Ir clusters are calculated and analyzed, the results show the bond strength follows Pd-Pd > Pd-Ir > Ir-Ir. The most stable configuration is Pd6Ir32 with all the core sites are occupied by Pd, which shows better electrochemical performance.

Chaotic neural networks and farfalle construction based parallel keyed secure hash function

AbstractParallel computing of hash functions along with the security requirements have great advantage in order to reduce the time consumption and overhead of the CPU. In this article, a keyed hash function based on farfalle construction and chaotic neural networks (CNNs) is proposed, which generates a hash value with arbitrary (defined by user) length (eg, 256 and 512 bits). The proposed hash function has parallelism merit because it is built over farfalle construction which avoids the dependency between the blocks of a given message. Moreover, the proposed hash function is chaos based (ie, it relies on chaotic maps and CNNs which have non‐periodic behavior). The security analysis shows that the proposed hash function is robust and satisfies the properties of hash algorithms, such as random‐like (non‐periodic) behavior, ideal sensitivity to original message and secret key, one‐way property and optimal diffusion effect. The speed performance of the hash function is also analyzed and compared with a hash function which was built based on sponge construction and CNN, and compared with secure hash algorithm (SHA) variants like SHA‐2 and SHA‐3. The results have shown that the proposed hash function has lower time complexity and higher throughput especially with large size messages. Additionally, the proposed hash function has enough resistance to multiple attacks, such as collision attack, birthday attack, exhaustive key search attack, preimage and second preimage attacks, and meet‐in‐the‐middle attack. These advantages make it ideal to be used as a good collision‐resistant hash function.

A Fast Route Conflict Detection Method based on Space Box

Routes Conflict detection is of great importance to air traffic control. Due to previous route conflict detection algorithm has a large number of computations, high time complexity and low reliability. This paper proposes a fast route conflict detection method based on the space box. Firstly, the space to be detected is divided into several space boxes, and the space box numbers of the track in the airspace are calculated according to the track coordinates. Secondly, in the case that multiple batches of tracks exist in the same space box, the location of the tracks is predicted according to the current track parameters. If the distance between the tracks gradually decreases, it is judged that the tracks have conflict risk and an alarm is required. The simulation results show that the algorithm has low time complexity, and high efficiency, and is suitable for parallel processing and large-scale route conflict detection, which has high application value.

A Lossless Compression and Encryption Method for Remote Sensing Image Using LWT, Rubik’s Cube and 2D-CCM

This paper proposes a lossless encryption–compression algorithm for large-scale remote sensing images. Firstly, the red, green and blue components of color image are compressed by a lossless predictive encoding. Then, the lifting wavelet transform (LWT) is used to decompose the encoding results. And a new Rubik’s cube transformation is introduced to scramble the decomposed coefficients, which uses the chaotic sequence generated by 2D Cubic–Chebyshev map (2D-CCM). The initial values of 2D-CCM are obtained from the chi-square test values of the three components, which leads to the algorithm related to the plaintext image. After that, the scrambling coefficients are thresholding, and the position sequences generated in the process are encrypted and compressed by the proposed encrypted run-length encoding (E-RLE). The processed coefficients are further compressed by Huffman encoding. At the end, the final results are obtained by a novel helix diffusion which is related to the chaotic sequence. Experimental results show that, this algorithm achieves higher lossless compression ratio with lower time complexity, and the encryption scheme has higher security.

Translational Compensation Algorithm for Ballistic Targets in Midcourse Based on Template Matching

The high-speed movement of a ballistic target will cause folding and translation of the micro-Doppler, which will affect the extraction of micro-motion features. To address the adverse effects of high-speed movement of ballistic targets in midcourse on the extraction of micro-motion features, a novel translational compensation algorithm based on template matching is proposed. Firstly, a 512 × 512 time-frequency map is obtained by binarization and down-sampling. The matching template then convolves the time-frequency map to obtain contour-like points. Then, the upper and lower contour points are preliminarily determined by the extreme value, and all actual contour points are screened out through structural similarity. Lastly, the upper and lower trend lines are determined and translation parameters for compensation by polynomial fitting are estimated. Simulation results show that the proposed algorithm has lower requirements for time-frequency resolution, higher precision and lower time complexity as a whole. Furthermore, it is also applicable to spectral aliasing.

Faster Activity and Data Detection in Massive Random Access: A Multiarmed Bandit Approach

This article investigates the grant-free random access mechanism for massive Internet of Things (IoT) devices. By embedding the data symbols in the signature sequences, joint device activity detection and data decoding can be achieved, which, however, significantly increases the computational complexity. Coordinate descent algorithms that enjoy a low per-iteration complexity have been employed to solve this detection problem, but previous works typically employ a random coordinate selection policy which leads to slow convergence. In this article, we develop multiarmed bandit (MAB) approaches for more efficient detection via coordinate descent, which achieves a delicate tradeoff between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exploration</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exploitation</i> in coordinate selection. Specifically, we first propose a bandit-based strategy, i.e., Bernoulli sampling, to speed up the convergence rate of coordinate descent, by learning which coordinates will result in more aggressive descent of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonconvex objective function</i> . To further improve the convergence rate, an inner MAB problem is established to learn the exploration policy of Bernoulli sampling. Both convergence rate analysis and simulation results are provided to show that the proposed bandit-based algorithms enjoy faster convergence rates with a lower time complexity compared with the state-of-the-art algorithm. Furthermore, our proposed algorithms are generally applicable to different scenarios, e.g., massive random access with low-precision analog-to-digital converters (ADCs).

Maximizing influence in social networks by distinguishing the roles of seeds

The problem of influence maximization in social networks has attracted much attention, and some algorithms have been proposed. However, the existing methods may be not suitable for large-scale networks because of high time complexity or failing to achieve the great performance. Given that the impacts of seeds in the loose neighbors (i.e., only including one-hop path with the candidate node) and the close neighbors (i.e., including one-hop and two-hop paths with the candidate node, and they form a closed triad) on the degree discount of candidate node are different. Moreover, when selecting multiple nodes as the seeds, we should not only consider the importance of the seeds themselves, but also ensure that the seeds are sufficiently dispersed to avoid the redundancy of propagation. To the end, we propose an efficient heuristic algorithm for influence maximization in social networks by considering redundancy weakening and two types of seeds into degree discount (named RWTDD). Based on the independent cascade model, the proposed RWTDD is compared with some well-known heuristic algorithms and greedy algorithms in six real social networks, experimental results indicate that the proposed RWTDD has a better influence coverage and low time complexity.