Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up t...
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Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.
All wireless communication systems are moving towards higher and higher frequencies day by day which are severely attenuated by rains in outdoor environment. To design a reliable RF system, an accurate prediction meth...
作者：
Nguyet, Nguyen Tran MinhBa, Dang Xuan
Faculty of Electrical and Electronics Engineering Department of Automatic Control Ho Chi Minh City71307 Viet Nam
Robotic manipulators are nonlinear systems with multi-input multi-output (MIMO) structures, uncertainties, and time-varying dynamical prospects. Unexpected influences of internal and external disturbances along with p...
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In the construction industry,to prevent accidents,non-destructive tests are necessary and *** impedance tomography is a new technology in non-invasive imaging in which the image of the inner part of conductive bodies ...
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In the construction industry,to prevent accidents,non-destructive tests are necessary and *** impedance tomography is a new technology in non-invasive imaging in which the image of the inner part of conductive bodies is reconstructed by the arrays of external electrodes that are connected on the periphery of the *** equipment is cheap,fast,and edge *** this imaging method,the image of electrical conductivity distribution(or its opposite;electrical impedance)of the internal parts of the target object is *** image reconstruction process is performed by injecting a precise electric current to the peripheral boundaries of the object,measuring the peripheral voltages induced from it and processing the collected *** an electrical impedance tomography system,the voltages measured in the peripheral boundaries have a non-linear equation with the electrical conductivity *** paper presents a cheap electrical Impedance Tomography(EIT)instrument for detecting impurities in the concrete.A voltage-controlled current source,a micro-controller,a set of multiplexers,a set of electrodes,and a personal computer constitute the structure of the *** conducted tests on concrete with impurities show that the designed EIT system can reveal impurities with a good accuracy in a reasonable time.
The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the...
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The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe
Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and netwo...
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Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score
Stochastic computing(SC)has a substantial amount of study on application-specific integrated circuit(ASIC)design for artificial intelligence(AI)edge computing,especially the convolutional neural network(CNN)***,SC has...
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Stochastic computing(SC)has a substantial amount of study on application-specific integrated circuit(ASIC)design for artificial intelligence(AI)edge computing,especially the convolutional neural network(CNN)***,SC has little to no optimization on field-programmable gate array(FPGA).Scaling up the ASIC logic without FPGA-oriented designs is inefficient,while aggregating thousands of bitstreams is still challenging in the conventional *** research has reinvented several FPGA-efficient 8-bit SC CNN computing architectures,i.e.,SC multiplexer multiply-accumulate,multiply-accumulate function generator,and binary rectified linear unit,and successfully scaled and implemented a fully parallel CNN model on Kintex7 *** proposed SC hardware only compromises 0.14%accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72%energy saving per image feedforward and 31?more data throughput than modern *** to SC,early decision termination pushed the performance baseline exponentially with minimum accuracy loss,making SC CNN extremely lucrative for AI edge computing but limited to classification *** SC's inherent noise heavily penalizes CNN regression performance,rendering SC unsuitable for regression tasks.
In this paper, two-and-three channel all-optical AND logic gates based on four-wave mixing in a highly nonlinear fiber for 120 Gbps on-off keying signals were designed, simulated, and investigated. Simulation results ...
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This work presents a comprehensive study of the application of multi-agent reinforcement learning (MARL) based on deep Q-networks (DQN), aiming to enhance the cooperation and coordination of multiple agents in complex...
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This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) ...
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