Data Hiding in DNA for Authentication of Plant Variety Rights

Hiding data in acid （DNA） can facilitate annotation of important plant the deoxyribose nucleic the authentication and variety rights. A grant of plant variety rights for a new plant variety gives you the exclusive right to produce for sale and sell propagating material of the variety. Digital watermarking techniques have been proposed for a wide range of applications, including ownership protection, copy control, annotation, and authentication. However, existing data hiding methods for DNA change the functionalities of DNA sequences, which induce morphological changes in biological patterns. This paper proposes a high capacity data hiding scheme for DNA without changing the functionalities of DNA sequences. This scheme adaptively varies the embedding process according to the amount of hidden data. Experimental results show that the proposed scheme gives a significantly improved hiding performance than previous schemes. And the robustness and security issues are also analyzed.

Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

Media Sharing Scheme with Cheater Identification and Content Reconstruction

For digital communication, distributed storage and management of media contents over system holders are critical issues. In this article, an efficient verifiable sharing scheme is proposed that can satisfy significant essentials of distribution sharing and can achieve a iossless property of host media. Verifiability allows holders to detect and identify counterfeited shadows during cooperation in order to prevent cheaters. Only authorized holders can reveal the lossless shared content and then reconstruct the original host image. Shared media capacity is adjustable and proportional to the increase of the number of the distributed holders t. The more distributed holders, the larger the shared media capacity is. Moreover, the ability to reconstruct the image preserves the fidelity of valuable host media, such as military and medical images. According to the results, the proposed approach can achieve superior performance to that of related sharing schemes for effectively providing distributed media management and storage.

Cooperative Spectrum Sensing in Cognitive Radio Using Bayesian Updating with Multiple Observations

We study cooperative spectrum sensing in cognitive radio (CR) networks using the hidden Markov model (HMM) for opportunistic spectrum access (OSA). We assume that the primary channel operates in a time division multiple address (TDMA) manner. Thus, spectrum sensing is operating in a slot-by-slot basis. In contrast to the conventional Bayesian updating using only one observation, in this work, we propose to perform the update in a concatenated fashion with all the observations available from the secondary users (SUs). In the proposed scheme, a predefined threshold on the belief is used for determining the channel activity. With the threshold, the proposed scheme is more flexible in the system operation than the simple majority vote scheme, in which no such threshold is available. We compare, by simulations, the performance of the proposed concatenated update scheme with that of the majority vote scheme and show that the probabilities of correctly detecting a busy state and an idle state are about 1 as the number of SUs is as large as 15, so the effects of the further increase in the number of SUs are limited.

Image Restoration Using Hybrid Features Improvement on Morphological Component Analysis

Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.

Edge Intelligence for 6G Networks

With the rapid development of smart terminals and infrastructures,as well as diversified applications(e.g.,autonomous driving,virtual and augmented reality,space-air-ground integrated networks)with colorful demands,current networks(e.g.,4G and 5G networks)may not be well suited to the requirements of novel applications and services.Recently,efforts from both the industry and academia have been made on the research into 6G networks,artificial intelligence(AI)will play a pivotal role in the design and optimization of 6G networks.