IET Communications最新文献

This paper investigates the changes in the waveform of a sinusoidal carrier resulting from amplitude modulation (AM) process. Based on this analysis, a novel method for extracting amplitude information is proposed. The proposed method uses the behaviour of the amplitude limitation which does not significantly affect the slope of the sinusoidal signal near zero crossing points. A simple comparator is used to convert the changes in sinusoidal slope near zero crossing points into pulse width changes. A simple circuit is proposed which keeps the output pulse width of the comparator constant by a simple control loop. The accuracy of the method is evaluated through simulation and is experimentally tested. If the modulation index is high and the amplitude of the input signal to the detector is limited, the proposed method can yield up at least 9 dB improvement in relative error power. However, if the modulation index is small, the improvement in relative error power can be at least 35 dB compared to other conventional types of AM demodulators.

Online social networks have become ubiquitous, allowing users to share opinions on various topics. However, oversharing can compromise privacy, leading to potential blackmail or fraud. Current platforms lack friend categorization based on trust levels. This study proposes simulating real-world friendships by grouping users into three categories: acquaintances, friends, and close friends, based on trust and engagement. It also introduces a dynamic method to adjust relationship status over time, considering users' past and present offenses against peers. The proposed system automatically updates friend lists, eliminating manual grouping. It calculates relationship strength by considering all components of online social networks and trust variations caused by user attacks. This method can be integrated with clustering algorithms on popular platforms like Facebook, Twitter, and Instagram to enable constrained sharing. By implementing this system, users can better control their information sharing based on trust levels, reducing privacy risks. The dynamic nature of the relationship status adjustment ensures that the system remains relevant as user interactions evolve over time. This approach offers a more nuanced and secure social networking experience, reflecting real-world relationship dynamics in the digital sphere.

Spectrum map is a database that stores multidimensional representations of spectrum situation information. It provides support for spectrum sensing and endows wireless communication networks with intelligence. However, the ubiquitous deployment of monitoring devices leads to huge costs of operation and maintenance. It indicates that an approach is needed to reduce the number of monitoring devices, but prevent the degradation of data granularity. Therefore, this paper focuses on the accurate construction of the spectrum map. It aims to infer the fine-grained spectrum situation of the target region based on coarse-grained observation. In order to solve this problem, an inference framework based on deep residual network is developed in this paper. In the case of rule deployment for sensing nodes, it adopts the idea of super resolution to improve the accuracy of the spectrum map. The framework is composed of two major parts: an inference network, which generates fine-grained spectrum maps from coarse-grained counterparts by using feature extraction module and upsampling construction module; and a fusion network, which considers the influence of environmental factors to further improve the performance. A large number of experiments on simulated datasets verify the effectiveness of the proposed method.

One of the most important problems in directional sensor networks is coverage problem. The coverage can be measured in two ways: positional or temporal. In temporal coverage, the directional sensors rotate periodically round themselves in a repetitive process. Thus, in each time slot, those targets that are positioned within the sensor nodes radius receive their desired coverage. In this model, if a target is left uncovered, it is said that the target has remained in darkness. The main task defined for the temporal coverage model is the minimization of the total dark time for all the targets in the network. This problem has been solved by greedy-based algorithms in last studies. Greedy-based algorithms are able to solve the temporal coverage problem in real time. Remember that the performance of greedy algorithms is extremely dependent on the closeness of optimal solution and initial candidates. For this reason, greedy algorithms may obtain local minima due to heuristic search. As far as we know meta-heuristic algorithms have not been used in past researches to solve such problems. For solving this problem, in this paper two algorithms were developed, GA-based and hybridized model comprising genetic algorithms and tabu search. A new model was suggested for the chromosome in genetic algorithm. To evaluate the performance of the developed algorithms, they were compared with randomized scenario and greedy-based algorithm presented in last studies. For better comparison, several parameters, including total dark time, number of sensors, number of targets, sector angle, sensing range were taken into account. The results obtained from the comparison of the algorithms indicated that the developed algorithms are effective in solving the temporal coverage problem in terms of minimizing the total dark time of the targets.

Kidney tumours are among the top ten most common tumours, the automatic segmentation of medical images can help locate tumour locations. However, the segmentation of kidney tumour images still faces several challenges: firstly, there is a lack of renal tumour endoscopic datasets and no segmentation techniques for renal tumour endoscopic images; secondly, the intra-class inconsistency of tumours caused by variations in size, location, and shape of renal tumours; thirdly, difficulty in semantic fusion during decoding; and finally, the issue of boundary blurring in the localization of lesions. To address the aforementioned issues, a new dataset called Re-TMRS is proposed, and for this dataset, the transformer-based boundary feedback network for kidney tumour segmentation (BFT-Net) is proposed. This network incorporates an adaptive context extract module (ACE) to emphasize local contextual information, reduces the semantic gap through the mixed feature capture module (MFC), and ultimately improves boundary extraction capability through end-to-end optimization learning in the boundary assist module (BA). Through numerous experiments, it is demonstrated that the proposed model exhibits excellent segmentation ability and generalization performance. The mDice and mIoU on the Re-TMRS dataset reach 91.1% and 91.8%, respectively.

This paper proposes a specific algebraic structure and demonstrates its nature as an extension field, enabling the construction of Golomb Costas (GC) arrays. It provides detailed instructions and examples for constructing GC arrays using this extension field, along with a corresponding flowchart. Additionally, the paper conducts a thorough analysis, incorporating calculations and comparisons, to evaluate the autocorrelation of a GC array derived from the extension field compared to that of a diagonal frequency hopping array. The analysis reveals the superior autocorrelation properties of GC arrays based on the extension field. Furthermore, the paper establishes a mathematical model for the signal coded by the frequency hopping array and subsequently simulates and compares the ambiguity function of the signal coded by a GC array with that of a signal coded by a diagonal frequency hopping array. This comparison underscores the thumbtack ambiguity function of frequency hopping signal coded by a GC array. Moreover, the paper thoroughly investigates the relationship between the correlation function of GC arrays and the roots of an algebraic equation in a finite field, and strictly proves the ideal autocorrelation properties of Golomb Costas arrays.

Flow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT.

Cloud computing has become an essential technology for people and enterprises due to the simplicity and rapid availability of services on the internet. These services are usually delivered through a third party, which provides the required resources for users. Therefore, because of the distributed complexity and increased spread of this type of environment, many attackers are attempting to access sensitive data from users and organizations. One counter technique is the use of intrusion detection systems (IDSs), which detect attacks within the cloud environment by monitoring traffic activity. However, since the computing environment varies from the environments of most traditional systems, it is difficult for IDSs to identify attacks and continual changes in attack patterns. Therefore, a system that uses an ensemble learning algorithm is proposed. Ensemble learning is a machine learning technique that collects information from weak classifiers and creates one robust classifier with higher accuracy than the individual weak classifiers. The bagging technique is used with a random forest algorithm as a base classifier and compared to three boosting classifiers: Ensemble AdaBoost, Ensemble LPBoost, and Ensemble RUSBoost. The CICID2017 dataset is utilized to develop the proposed IDS to satisfy cloud computing requirements. Each classifier is also tested on various subdatasets individually to analyze the performance. The results show that Ensemble RUSBoost has the best average performance overall with 99.821% accuracy. Moreover, bagging achieves the best performance on the DS2 subdataset, with an accuracy of 99.997%. The proposed model is also compared to a model from the literature to show the differences and demonstrate its effectiveness.

Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real-time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real-time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi-objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing.

In vehicular communications, channel estimation is a complex problem due to the joint time–frequency selectivity of wireless propagation channels. To this end, several signal processing techniques as well as approaches based on neural networks have been proposed to address this issue. Due to the highly dynamic and random nature of vehicular communication environments, precise characterization of temporal correlation across a received data sequence can enable more accurate channel estimation. This paper proposes a new pilot constellation scheme in combination with a small feed-forward neural network to improve the accuracy of channel estimation in V2X systems while keeping low the implementation complexity. The performance is evaluated in typical vehicular channels using simulated BER curves, and it is found superior to traditional channel estimation methods and state-of-the-art neural-network-based implementations such as feed-forward and super-resolution. It is illustrated that the improvement becomes pronounced for small subcarrier spacings (or low 5G numerologies); hence, this paper contributes to the development of more reliable mobile services across rapidly varying vehicular communication channels with rich multi-path interference.