The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the ...complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.
This paper designs a smart urban environment monitoring system based on the wireless network of ZigBee to complete the real-time collection of urban environment information. The system consists of ...the basic monitoring network and the remote receiving terminal. The basic monitoring network connects the streetlights as routes and the taxis as nodes. After dynamically organizing the network, each node is assigned with an address as the only identity in the network. Then, the system designed conducts the simulation experiment to prove that it could meet the needs and send the collected information to the designated terminal in the form of message according to the setting. The sensor organized through the wireless network of ZigBee could inspire the infrastructure construction of the smart city. With the network, a smarter and more comfortable society could be well offered to people.
The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both ...unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.
With the rapid development of the Internet of Things (IoT), the frequency of attackers using botnets to control IoT devices in order to perform distributed denial-of-service attacks (DDoS) and other ...cyber attacks on the internet has significantly increased. In the actual attack process, the small percentage of attack packets in IoT leads to low accuracy of intrusion detection. Based on this problem, the paper proposes an oversampling algorithm, KG-SMOTE, based on Gaussian distribution and K-means clustering, which inserts synthetic samples through Gaussian probability distribution, extends the clustering nodes in minority class samples in the same proportion, increases the density of minority class samples, and improves the amount of minority class sample data in order to provide data support for IoT-based DDoS attack detection. Experiments show that the balanced dataset generated by this method effectively improves the intrusion detection accuracy in each category and effectively solves the data imbalance problem.
As a multi hop self-organizing network, wireless sensor network has the ability to cooperatively sense, collect and process the information of the sensed objects. The applications of WCN in 5G-based ...Internet of Vehicles (5G-IoV), using information fusion and intelligent information processing technologies, can obtain more reliable and accurate detection parameters, which has been widely concerned. However, the massive connectivity and information exchange in 5G-IoV pose great challenges to the bandwidth efficiency. In order to overcome these issues in 5G-IoV networks, a performance enhanced scheme based on non-orthogonal multiple access (NOMA) is proposed. In the proposed scheme, different vehicle locations are respectively discussed, i.e., whether in the overlap region of cluster head vehicles (CHVs). In particular, different to conventional works, each receiving node only decodes the desired signal to avoid performance loss provided from the poor channel quality limitation. On the other hand, all CHVs decode-and-forward new superposition coded signals with new power allocation factors, while that the maximum ratio combining is utilized at receivers to further improve the ergodic sum-rate (SR) and probability of conflict. The closed-form expressions of ergodic SR for our proposed scheme are analyzed under the independent Rayleigh fading channels. Numerical results corroborating our theoretical analysis show that the superposition coded signal transmission scheme applied to the proposed NOMA-IoV improves the ergodic SR performance significantly compared with the existing works, especially for the high signal-to-noise region.
An on-chip, high-extinction-ratio transverse electric (TE) pass polarizer utilizing a silicon oxynitride (SiON) slab has been proposed and experimentally verified. The power confinement ratio of the ...mode field is manipulated by using a SiON slab, where most of the power of the transverse magnetic (TM) mode is transferred to the upper SiON slab and then attenuated through radiation, while the TE mode passes through with relatively low propagation loss. Experimental results show that our proposed device can achieve an extinction ratio that varies from 20.5 to 32.7 dB in the wavelength range of 790 to 870 nm, with an insertion loss of 0.6 to 1.7 dB. Potentially, this design has lower material refractive index contrast, larger minimum etching size, smaller lengths, and less stray light crosstalk, which is beneficial for systems applications such as gyroscopes.
Mode division multiplexing (MDM) technology is becoming increasingly important for modern optical communication systems. Here, an ultra-compact broadband in-line mode converter for quasi-TE 00 and ...quasi-TE 10 on the silicon-on-insulator platform is proposed and demonstrated experimentally. In our device, the mode-conversion region consists of a continuously width-modulated waveguide with a footprint size as small as 1.32 × 4.52 μm 2 . Its modulation profile is designed by using the particle swarm optimization algorithm. This device has a simulated conversion efficiency of about −0.174 dB and an insertion loss less than 0.153 dB within 100-nm wavelength bandwidth from 1500 nm to 1600 nm. Our design exhibits a favorable fabrication error tolerance and the fabricated device has achieved nearly the same conversion efficiency as the simulated one. Our concept can also be applied to design other high-performance mode converters, i.e., converting modes between quasi-TE 20 and quasi-TE 00 . Our work suggests a very promising path for realizing compact integrated MDM systems.
One of the five types of Internet information service recommendation technologies is the personalized recommendation algorithm, and knowledge graphs are frequently used in these algorithms. RippleNet ...is a personalized recommendation model based on knowledge graphs, but it is susceptible to localization issues in user portrait updating. In this study, we propose NRH (Node2vec-side and RippleNet Hybrid Model), a hybrid recommendation model based on RippleNet that uses Node2vec-side for item portrait modeling and explores potential association relationships of items; the user portrait is split into two parts, namely, a static history portrait and a dynamic preference portrait; the NRH model adopts a hybrid recommendation approach based on collaborative filtering and a knowledge graph to obtain the user’s preferences on three publicly accessible datasets; and comparison experiments with the mainstream model are lastly carried out. The AUC and ACC increased, respectively, by 0.9% to 29.5% and 1.6% to 31.4% in the MovieLens-1M dataset, by 1.5% to 17.1% and 4.4% to 18.7% in the Book-Crossing dataset, and by 0.8% to 27.9% and 2.9% to 24.1% in the Last.FM dataset. The RippleNet model was used for comparison experiments comparing suggestion diversity. According to the experimental findings, the NRH model performs better in accuracy and variety than the popular customized knowledge graph recommendation algorithms now in use.
Tropical cyclones (TCs) exert a significant influence on the upper ocean, leading to sea surface temperature (SST) changes on a global scale. However, TC-induced SST responses exhibit considerable ...variability in the northern Indian Ocean (NIO), and the general understanding of these responses remains limited. This paper investigates the SST changes caused by 96 TCs over an 18-year period in the NIO. Through a composite analysis utilizing satellite SST data, a comprehensive study is conducted to examine the relationship between TC characteristics, including wind speed and translation speed, and the associated SST changes. The overall findings reveal that within a radius of 300 km from the TC center, SST decreases were observed at 1702 (86%) locations, with an average SST response to TC of −0.46 °C and a maximum decrease of −2.07 °C. The most significant reduction in SST typically occurred two days after the passage of TCs, followed by a gradual recovery period exceeding 15 days for the SSTs to return to their initial values. Consistent with findings in other ocean basins, stronger and slower-moving TCs induced more substantial cooling effects. Conversely, at 279 (14%) locations, particularly associated with TCs of weaker intensities, SST increases were observed following the TC passage. Notably, 140 of these locations were situated at low latitudes, specifically between 8° N and 15° N. This study provides a quantitative analysis of the comprehensive SST response to TCs in the NIO.
In order to effectively solve the current security problems encountered by smart wireless terminals in the digital twin biological network, to ensure the stable and efficient operation of the ...wireless communication network. This research aims to reduce the interference attack in the communication network, an interference source location scheme based on Mobile Tracker in the communication process of the Internet of Things (IoT) is designed. Firstly, this paper improves Attribute-Based Encryption (ABE) to meet the security and overhead requirements of digital twin networking communication. The access control policy is used to encrypt a random key, and the symmetric encryption scheme is used to hide the key. In addition, in the proposed interference source location technology, the influence of observation noise is reduced based on the principle of unscented Kalman filter, and the estimated interference source location is modified by the interference source motion model. In order to further evaluate the performance of the method proposed as the interference source, this paper simulates the jamming attack scenario. The Root Mean Square Error (RMSE) value of the proposed algorithm is 0.245 m, which is better than the ErrMin algorithm (0.313 m), and the number of observation nodes of the proposed algorithm is less than half of the ErrMin algorithm. To sum up, satisfactory results can be achieved by taking the Jamming Signal Strength (JSS) information as the observation value and estimating the location of the interference source and other state information based on the untracked Kalman filter algorithm. This research has significant value for the secure communication of the digital twins in the IoT.
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