Deep transfer modeling for classification of Maize Plant Leaf Disease

Multimedia Tools and Applications - Currently, Deep Learning is playing an influential role for Image analysis and object classification. Maize’s diseases reduce production that subsequently...     

IRS辅助的SWIPT系统中基于能效优先的波束成形设计与优化

以智能反射面(intelligent reflecting surface,IRS)辅助的无线携能通信(simultaneous wireless information and power transfer,SWIPT)系统为背景,研究了该系统中基于能效优先的多天线发送端有源波束成形与IRS无源波束成形联合设计与优化方法。以最大化接收端的最小能效为优化目标,构造在发送端功率、接收端能量阈值、IRS相移等多约束下的非线性优化问题,用交替方向乘子法(alternating direction method of multipliers,ADMM)求解。采用Dinkelbach算法转化目标函数,通过奇异值分解(singular value decomposition,SVD)和半定松弛(semi-definite relaxation,SDR)得到发送端有源波束成形向量。采用SDR得到IRS相移矩阵与反射波束成形向量。结果表明,该系统显著降低了系统能量收集(energy harvesting,EH)接收端的能量阈值。当系统总电路功耗为?15 dBm时,所提方案的用户能效为300 KB/J。当IRS反射阵源数与发送天线数均为最大值时,系统可达最大能效。     

Application of big data language recognition technology and GPU parallel computing in English teaching visualization system

International Journal of Speech Technology - With the development of multimedia technology and network technology applications, it is possible to implement online teaching systems in schools. This...     

有机-无机压电材料TMCM-MnCl3的研究

有机-无机压电材料是一种分子铁电体，具有柔性、结构灵活、易成膜、全液相合成及环保节能等优点，可满足新一代薄膜器件及可穿戴设备的需求。该文以三甲基卤代甲基铵(TMXM, X=F, Cl, Br)为有机部分，MnCl₂为无机部分，通过溶液蒸发法制备了具有钙钛矿分子结构的有机-无机压电材料三甲基氯三氯化锰(TMCM-MnCl₃)，并对其分子结构组成、压电、热学、声学及铁电性进行表征。结果表明，TMCM-MnCl₃的压电常数为106 pC/N，居里温度为130 ℃，声阻抗值约为16.5 MRayl，低于压电陶瓷PZT-4(大于33 MRayl)，具有广阔的应用前景。     

VLC Enabled Hybrid Wireless Network for B5G/6G Communications

Wireless Personal Communications - In the present scenario, there is a boom in the demand of the users to achieve increased capacity, high data, low latency, and high-performance rates. 5G New...     

A privacy-preserving model based on differential approach for sensitive data in cloud environment

A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners’ data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.