An emerging modality in cancer treatment is tumor treating fields (TTF). It consists of electric fields of approximately 1 VRMS/cm in strength and of around 200 kHz in frequency. When these fields are aligned with a c...
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Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interf...
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In this paper, a quality diversity optimization method (QDOM) based on an adaptive bound-searching algorithm and diversity-selecting immune algorithm is proposed for solving bilinear matrix inequality (BMI) problems i...
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In late 2019, COVID-19 virus emerged as a dangerous disease that led to millions of fatalities and changed how human beings interact with each other and forced people to wear masks with mandatory lockdown. The ability...
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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...
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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
Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement...
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Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement in realistic multipath environments. Array decorrelation techniques have been proposed, achieving correlation reductions by either tilting the antenna beams or shifting the phase centers away from each other. Hence, these methods are mainly limited to MIMO terminals with small arrays. To avoid such problems, this work proposes a decorrelation optimization technique based on phase correcting surface(PCS)that can be applied to large MIMO arrays, enhancing their MIMO performances in a realistic(non-isotropic)multipath environment. First, by using a near-field channel model and an optimization algorithm, a near-field phase distribution improving the MIMO capacity is obtained. Then the PCS(consisting of square elements)is used to cover the array's aperture, achieving the desired near-field phase *** examples demonstrate the effectiveness of this PCS-based near-field optimization technique. One is a1 × 4 dual-polarized patch array(working at 2.4 GHz)covered by a 2 × 4 PCS with 0.6λ center-to-center distance. The other is a 2 × 8 dual-polarized dipole array, for which a 4 × 8 PCS with 0.4λ center-to-center distance is designed. Their MIMO capacities can be effectively enhanced by 8% and 10% in single-cell and multi-cell scenarios, respectively. The PCS has insignificant effects on mutual coupling, matching, and the average radiation efficiency of the patch array, and increases the antenna gain by about 2.5 dB while keeping broadside radiations to ensure good cellular coverage, which benefits the MIMO performance of the *** proposed technique offers a new perspective for improving large MIMO arrays in realistic multipath in a statistical sense.
Battery management systems require mathematical models of the battery cells that they monitor and control. Commonly, equivalent circuit models are used. We would like to be able to determine the parameter values of th...
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This paper explores the integrated attitude-position formation control problem of multiple vehicles, where individual entities within the formation have heterogeneous objectives. The six-degree-of-freedom multi-vehicl...
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We propose a protocol in quantum illumination (QI) leveraging entanglement in discrete-variable states. Our investigation shows that, as M→∞, the M-mode Bell state matches the 6 dB advantage of the two-mode squeezed...
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We propose a protocol in quantum illumination (QI) leveraging entanglement in discrete-variable states. Our investigation shows that, as M→∞, the M-mode Bell state matches the 6 dB advantage of the two-mode squeezed vacuum in high noise. It also excels in low- and mid-noise conditions, demonstrating that QI's benefits are not restricted to high background noise. Moreover, the protocol benefits from a sequential decision rule, increasing the advantage beyond 6 dB. These findings present an intriguing alternative to continuous-variable states and open different applications for QI using discrete-variable states.
Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks ge...
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