The Way to Apply Machine Learning to IoT Driven Wireless Network from Channel Perspective

Internet of Things(IoT) is one of the targeted application scenarios of fifth generation(5 G) wireless communication.IoT brings a large amount of data transported on the network.Considering those data,machine learning(ML) algorithms can be naturally utilized to make network efficiently and reliably.However,how to fully apply ML to IoT driven wireless network is still open.The fundamental reason is that wireless communication pursuits the high capacity and quality facing the challenges from the varying and fading wireless channel.So in this paper,we explore feasible combination for ML and IoT driven wireless network from wireless channel perspective.Firstly,a three-level structure of wireless channel fading features is defined in order to classify the versatile propagation environments.This three-layer structure includes scenario,meter and wavelength levels.Based on this structure,there are different tasks like service prediction and pushing,self-organization networking,self adapting largescale fading modeling and so on,which can be abstracted into problems like regression,classification,clustering,etc.Then,we introduce corresponding ML methods to different levelsfrom channel perspective,which makes their interdisciplinary research promisingly.

Robust detection algorithm with triple constraints for cooperative target based on spectral residual

The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.

Targeting histone deacetylases for cancer therapy: Trends and challenges

Dysregulation of histone deacetylases(HDACs) is closely related to tumor development and progression. As promising anticancer targets, HDACs have gained a great deal of research interests and two decades of effort has led to the approval of five HDAC inhibitors(HDACis). However, currently traditional HDACis, although effective in approved indications, exhibit severe off-target toxicities and low sensitivities against solid tumors, which have urged the development of next-generation of HDACi. This review investigates the biological functions of HDACs, the roles of HDACs in oncogenesis, the structural features of different HDAC isoforms, isoform-selective inhibitors, combination therapies, multitarget agents and HDAC PROTACs. We hope these data could inspire readers with new ideas to develop novel HDACi with good isoform selectivity, efficient anticancer effect, attenuated adverse effect and reduced drug resistance.