An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

With increasingly smaller size, more powerful sensing capabilities and higher level of autonomy, multiple unmanned aerial vehicles (UAVs) can form UAV networks to collaboratively complete missions more reliably, efficiently and economically. While UAV networks are promising for many applications, there are many outstanding issues to be resolved before large scale UAV networks are practically used. In this paper we study the application of cognitive radio technology for UAV communication networks, to provide high capacity and reliable communication with opportunistic and timely spectrum access. Compressive sensing is applied in the cognitive radio to boost the performance of spectrum sensing. However, the performance of existing compressive spectrum sensing schemes is constrained with non-strictly sparse spectrum. In addition, the reconstruction process applied in existing schemes has unnecessarily high computational complexity and low energy efficiency. We proposed a new compressive signal processing algorithm, called Iterative Compressive Filtering, to improve the UAV network communication performance. The key idea is using orthogonal projection as a bandstop filter in compressive domain. The components of primary users (PUs) in the recognized subchannels are adaptively eliminated in compressive domain, which can directly update the measurement for further detection of other active users. Experiment results showed increased efficiency of the proposed algorithm over existing compressive spectrum sensing algorithms. The proposed algorithm achieved higher detection probability in identifying the occupied subchannels under the condition of non-strictly sparse spectrum with large computational complexity reduction, which can provide strong support of reliable and timely communication for UAV networks

The restricted EM algorithm under inequality restrictions on the parameters

AbstractOne of the most powerful algorithms for maximum likelihood estimation for many incomplete-data problems is the EM algorithm. The restricted EM algorithm for maximum likelihood estimation under linear restrictions on the parameters has been handled by Kim and Taylor (J. Amer. Statist. Assoc. 430 (1995) 708–716). This paper proposes an EM algorithm for maximum likelihood estimation under inequality restrictions A0β⩾0, where β is the parameter vector in a linear model W=Xβ+ε and ε is an error variable distributed normally with mean zero and a known or unknown variance matrix Σ>0. Some convergence properties of the EM sequence are discussed. Furthermore, we consider the consistency of the restricted EM estimator and a related testing problem

Authenticated Key Agreement Protocol Based on a Matrix Group and Polynomial Ring over a Finite Field

Alongside encryption and signatures, key agreement is one of the fundamental issues in modern cryptography and its security is the main concern in cloud computing and World Wide Web-based applications. In this paper, a novel type of more secure 3-pass key agreement protocol is proposed based on a recently proposed matrix-based key agreement protocol of Romańczuk and Ustimenko. By the hash-and-sign approach and immediate use of new session key, explicit key authentication, forward secrecy and bit security are achieved simultaneously. Cryptanalysis also shows that it is immune to the man-in-the-middle attack while matrix entries from a commutative ring provide an advantageous hiding mechanism

Recurrent exercise-induced acute kidney injury by idiopathic renal hypouricemia with a novel mutation in the SLC2A9 gene and literature review

OBJETIVO: Comparar a sensibilidade do método de difusão em ágar e do método de extração utilizando as linhagens celulares RC-IAL (células fibroblásticas de rim de coelho) e HeLa (células epiteliais de carcinoma do colo do útero humano), na avaliação da citotoxicidade "in vitro" de materiais de uso médico-hospitalar. MATERIAL E MÉTODO: Foram testadas 50 amostras escolhidas por sorteio, entre as já conhecidamente positivas e negativas e identificadas como: algodão, espuma, borracha, látex, celulose e acrílico. Além, das amostras citadas foram testadas experimentalmente várias concentrações de SDS (duodecil sulfato de sódio) nas culturas celulares RC-IAL e HeLa. RESULTADOS: Das 50 amostras testadas , 44 (88%) foram positivas para os dois métodos. Mas quando comparado o SDS nos dois métodos foram observados resultados positivos nas concentrações de 0,5 a 0,05 µg/ml no método de difusão em ágar e no método de extração somente foi observado efeito citotóxico até a concentração de 0,25 µg/ml. CONCLUSÃO: Os resultados encontrados são similares aos observados por outros autores que testaram materiais como, por exemplo, ligas metálicas. Quando foi usado o SDS observou-se, nas duas linhagens celulares, diferenças favoráveis ao método de difusão em ágar em duas concentrações, isto é, a sensibilidade deste método foi significantemente maior, por inspecção, em relação ao método de extração, além de se constituir em método mais simples de ser realizado

Deep learning classification and recognition method for milling surface roughness combined with simulation data

To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes