In recent years, the investigations on cyber-physical systems (CPS) have become increasingly popular in both academia and industry. A primary obstruction against the booming deployment of CPS applications lies in how to process and manage large amounts of generated data for decision making. To tackle this predicament, researchers advocate the idea of coupling edge computing, or edge-cloud computing into the design of CPS. However, this coupling process raises a diversity of challenges to the quality-of-services (QoS) of CPS applications. In this article, we present a survey on edge computing or edge-cloud computing assisted CPS designs from the QoS optimization perspective. We first discuss critical challenges in service latency, energy consumption, security, privacy, and reliability during the integration of CPS with edge computing or edge-cloud computing. Afterwards, we give an overview on the state-of-the-art works tackling different challenges for QoS optimization, and present a systematic classification during outlining literature for highlighting their similarities and differences. We finally summarize the experiences learned from surveyed works and envision future research directions on edge computing or edge-cloud computing assisted CPS optimization.

Different subjects may express a specific expression in different ways due to inter-subject variabilities. In this work, besides training deep-learned facial expression feature (emotional feature), we also consider the influence of latent face identity feature such as the shape or appearance of face. We propose an identity and emotion joint learning approach with deep convolutional neural networks (CNNs) to enhance the performance of facial expression recognition (FER) tasks. First, we learn the emotion and identity features separately using two different CNNs with their corresponding training data. Second, we concatenate these two features together as a deep-learned Tandem Facial Expression (TFE) Feature and feed it to the subsequent fully connected layers to form a new model. Finally, we perform joint learning on the newly merged network using only the facial expression training data. Experimental results show that our proposed approach achieves 99.31% and 84.29% accuracy on the CK+ and the FER+ database, respectively, which outperforms the residual network baseline as well as many other state-of-the-art methods.

Logistics creates tremendous economic value through supporting the trading of goods between firms and customers, thereby improving the welfare of the society. In order to continuously improve the quality of logistics service, a great variety of cyber-physical techniques have been utilized in the modern logistics systems, which help tackle the grand challenges in multiple aspects including time efficiency, economic cost, safety risk and cyber-security. The fast development of artificial intelligence (AI) has gained significant popularity and success in various domains, and hence, it has been adopted to construct high-quality solutions that can facilitate the monitoring, operation and decision in logistics systems. Furthermore, the deployment of advanced sensoring and computing components forms a cyber-physical system (CPS) infrastructure, which promotes the capability and scalability on data acquisition, transmission, storage and processing, thereby enabling the further penetration of AI technologies. Based on the existing advances, this paper is devoted to conducting a comprehensive survey of the AI applications in the modern logistics CPSs. In specific, we focus on the AI-based research and industrial solutions that can improve the time/economic efficiency, safety and cyber-security of logistics systems. The potential applications of AI to tackle the remaining challenges are also discussed to investigate the way to continuously improving the quality of logistics service.

In this paper, we propose a novel methodology for detecting systematic failure patterns at the wafer level for yield learning. Our proposed methodology takes the binary testing results (i.e., pass or fail) of all dies over multiple wafers, cluster these wafers according to their spatial signatures of failures, and eventually identify the underlying systematic failure patterns. Several data processing techniques, including singular value decomposition, hierarchical clustering, etc., are adopted to make the proposed methodology robust to random failures. In addition, a Pseudo-Boolean satisfiability solver is used to extract a minimal set of systematic failure patterns that explain all wafer-level spatial signatures. These patterns help process engineers identify the root causes of failures and accelerate yield learning. The efficacy of our proposed approach is demonstrated by one synthetic data set and one industrial data set.

In this paper, a graph-constrained sparse performance modeling method is proposed for analog circuit optimization. It builds sparse polynomial models constrained by an acyclic graph. These models can be used to solve analog optimization problems within local design spaces by using convex semidefinite programming (SDP) relaxation both efficiently and robustly. Our numerical examples demonstrate that the proposed modeling and optimization method can quickly and accurately converge to a superior solution for analog circuits while the conventional method fails to work.