Electronic nose (E-nose) plays an important role in the detection of Chinese baijiu, which is an alcoholic beverage of high reputation. However, traditional e-nose data processing relies on manual selection of features with complicated preprocessing steps. The production process of baijiu with the same flavor is similar, but due to the limited number of sensors, it is not easy to quickly extract specific features with e-nose. To overcome this shortcoming, we propose a novel method based on Channel Attention Convolutional Neural Network (CA-CNN) for authenticity identification of Chinese baijiu. The underlying channel attention module analyzes the dependencies between channels and learning weights in order to improve the detection accuracy. In particular, we evaluated the comprehensive performance of the system by comparing it with traditional machine learning methods. The prediction accuracy of the CA-CNN (98.53%) is better than the back-propagation artificial neural network (BP-ANN) (90.83%), the support vector machine (SVM) (84.5%), and the random forest (RF) (86.867%). The experiment results show that the CA-CNN has reasonable reliability, stability and good prediction performance in the quality classification of Chinese baijiu. It provides an effective reference method for the standardization of baijiu quality inspection.

Owing to the increasing popularity of online social media, Web 3.0, and Metaverse, mobile users can access intelligent and self-sustained services more conveniently than before. However, the enormous computation burden and data flow associated with these services pose tremendous challenges for future 6G networks that strive to enable diversified applications with varying user requirements. To address these grave challenges, wireless edge caching and computing are widely acknowledged as the two most successful enablers. Meanwhile, the trend of composing large services/applications as a suite of small and independent microservices (MSs) is leading to the cases where functional units can be distributed over edges, accelerating the transition of telecom architecture towards distributed MS-based paradigm. This article conceives an overarching perspective towards such distributed MS-aware cellular networks (DMCNs), which embrace the conventional service caching (and computing) system as a special case. The studies are expanded based on two aspects. One is the fundamental analysis of network architecture and the other is the leading design principles, including the user’s behavior modeling, MS placement, MS migration, and the joint consideration. These key issues are compelling problems in practices, yet have been largely ignored by existing works. Several potential solutions to the aforementioned issues are presented, and by addressing the challenges and exploring the potential enablers, we can pave the way for the successful implementation and adoption of DMCNs in the future. The article also includes a pair of case studies to validate the effectiveness of the developed framework. Finally, research opportunities and future trends are discussed.

Odor descriptors are words used to express human olfactory perception. At a certain level, predicting the odor descriptor rating using an electronic nose(E-nose) equips the machine with the ability to perceive odors. In this paper, we propose a novel deep learning framework for predicting odor descriptor rating via E-nose signals. The proposed framework has multiple sibling neural networks, including a Convolutional LSTM(ConvLSTM) layer and a regression layer. The ConvLSTM layers are used to learn the spatiotemporal features from sensor signal. On the one hand, it effectively extracts the temporal features of the signal, and on the other hand, it makes good use of the sensor dependencies. The output of the regression layer corresponds to a combination of descriptors or single descriptors, depending on their similarity. The experiment results show that: i) the framework utilizing the spatiotemporal correlation of the E-nose signal is more effective than using the temporal or spatial model alone, and that ii) the combination of descriptors based on descriptors similarity can effectively improve the prediction accuracy. On the DREAM dataset, our proposed framework outperforms the state-of-the- art methods in odor descriptor rating prediction.

Predicting odor’s pleasantness with electronic nose can simplify the evaluation process of odors, and it has potential applications in the perfumes and environmental monitoring industry. Classical algorithms for predicting odor’s pleasantness generally use a manual feature extractor and an independent classifier. The feature extractor is the key to developing accurate algorithms. However, its design requires expertise and experience. In order to circumvent this difficulty, we propose a model for predicting odor’s pleasantness by using convolutional neural network. It was found that our model, which uses convolutional neural layers, outperforms manual feature extractor. Experiment results showed that the correlation between our model and human was over 90% on pleasantness rating. Our model also achieved an accuracy of 99.9% in distinguishing between absolutely pleasant and unpleasant odors.

Diverse applications are used on mobile devices. Because of the increasing dependence on information systems, immense amounts of personal and sensitive data are stored on mobile devices. Thus, security or privacy breaches are a major challenge. To protect mobile systems and the private information on these systems from being accessed by adversaries, a framework for mobile user identification through the use of a multimodal behavioural biometrics scheme with a keystroke trajectory feature is presented herein. Conventionally, mobile devices have been protected by mechanisms such as PINs or passwords. However, these approaches have numerous disadvantages. Therefore, approaches that employ keystroke biometrics for secure and reliable mobile device identification have been proposed. Because unimodal behavioural biometrics identification mechanisms have limited accuracy and effectiveness, a multimodal scheme that includes different behavioural biometric traits, such as keystroke and swipe biometric traits, is examined. However, the information provided by the spatial and temporal features of keystroke biometrics is not comprehensive. Therefore, a trajectory model is derived to describe the behavioural biometric uniqueness of a user. In the user identification phase, a multistream recurrent neural network (RNN) is adopted. The results reveal that the proposed trajectory model performs well, and the multimodal scheme using an RNN with a late fusion method provides accurate identification results. The proposed system achieved an accuracy of 95.29%, F1 score of 94.64%, and equal error rate of 1.78%. Thus, the proposed mobile identification system is capable of resisting attacks that standard mechanisms may be vulnerable to and represents a valuable contribution to cyber security.