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Interconnect delay is a key factor that affects the chip performance in layer assignment. Particularly in the advanced process technologies of 5 nm and beyond, interconnect delay has grown significantly due to the increase of circuit scale. Moreover, coupling effect existed in wires reduces the accuracy of delay evaluation. On the other hand, the size of vias is often ignored in layer assignment, which enlarges the mismatch between global routing and detailed routing. To solve these problems, we propose VPT, a timing-aware layer assignment algorithm considering via pillars, which includes the following five key techniques: 1) via pillar structure combined with nondefault-rule (NDR) wires is adopted to form a net delay optimization system for advanced process technologies; 2) a synthetical model that can adapt to varying types and sizes of both vias and wires is designed to evaluate overflow effectively; 3) a sorting strategy is devised to reduce uncertainty of layer assignment flow and improve stability of the proposed algorithm; 4) an awareness strategy based on multiaspect congestion assessment is designed to reduce overflow significantly; and 5) a net scalpel algorithm is devised to minimize the maximum delay of nets, so that the timing behaviors can be improved systematically. The experimental results on multiple benchmarks confirm that the proposed algorithm leads to lower delay and less overflow, while achieving the best solution quality among the existing algorithms with the shortest runtime. [ABSTRACT FROM AUTHOR]

Sports injuries can be a major problem for athletes. Therefore, sports injury protection has become a key focus of attention in sports and medical circles. With widening participation in sport, related injuries can have an impact at national level. However, many areas around the world lack adequate medical resources. It takes more time and money for local people to get to the nearest rehabilitation department or physical therapy studio. Artificial intelligence (AI) has undergone vigorous development, leading to increased computing speed and accuracy. Nowadays, two-dimensional image signals can be used for body-posture recognition. This research is based on the Openpose limb-detection AI model, which has corrective exercise training elements and uses functional motion-detection technology as the diagnostic basis (combined with physical therapists' clinical knowledge of rehabilitation interventions). We propose a 2-D imaging physical health detection system. The system is divided into four main parts: a mobile app user interface, the computing server, professional interface and database. A user video is recorded by the app. The computing server then calculates keypoints of the human body through Openpose and converts these data into clinical test indicators. Finally, health indicators are displayed, and the app registers action scores. The professional interface presents users with health feedback and recommends suitable rehabilitation videos. The computing server is divided into two parts: limb detection and the detection system. The limb-detection aspect is divided into four parts: sample collection, video processing, keypoint processing and results comparison. The detection system is subdivided into system architecture and a health evaluation (forming the basis for video recommendations). The key contributions of this paper are that the proposed system can calculate body posture and automatically detect the physical condition and health of the body. In addition to reducing dependence on professional human resources, the system can also save the trouble of manual angle measurement in traditional physical therapy. [ABSTRACT FROM AUTHOR]

Continuous-flow microfluidic biochips (CFMBs), also known as lab-on-a-chip platforms, have attracted considerable attentions in the past two decades and have been widely used in various laboratory procedures in biomedicine and biochemistry such as point-of-care diagnosis and protein crystallization. Besides a completely automatic execution procedure, these micrometerscale devices offer several advantages over the conventional fluidic platforms, e.g., low sample/reagent consumption, low manufacturing cost, and high execution efficiency. However, similar to the early development of integrated circuits, as the feature size of biochips keeps shrinking, tens of thousands microvalves can now be integrated into a single chip. Consequently, traditional manual-based chip design is no longer suitable and Computer-aided Design (CAD) tools have to be introduced to deal with the large-scale integration of such chips. This paper presents a comprehensive review of recent advances in the design automation of CFMBs, including CAD techniques for architecture synthesis, volume assignment and sample preparation, testing, fault-tolerant design, and washing. These tools have great significance in advancing the automation level of biochip design and further promote their wide adoption. Finally, future trends and potential research topics are discussed in detail to further improve the performance of CFMBs. [ABSTRACT FROM AUTHOR]

The Steiner minimal tree (SMT) problem is an NP-hard problem, which is the best connection model for a multi-terminal net in global routing problem. This paper presents a unified algorithm for octagonal and rectilinear SMT construction based on hybrid transformation strategy (HTS) and self-adapting particle swarm optimization. Firstly, an effective HTS is proposed to enlarge the search space and improve the convergence speed. Secondly, the proposed HTS in the evolutionary process may produce an ineffective solution, and consequently the crossover and mutation operators of genetic algorithm (GA) based on union-find sets is proposed. Thirdly, a self-adapting strategy that can adjust the acceleration coefficients is proposed to further improve the convergence and the quality of the proposed algorithm. Finally, the hybrid transformation can be applied to GA and the proposed algorithm can be applied to rectilinear architecture. To our best knowledge, the proposed algorithm is the first unified algorithm to solve the SMT construction under both octagonal and rectilinear architecture. The experimental results show that the proposed algorithm can efficiently provide a better solution for SMT problem both in octagonal and rectilinear architectures than others. Moreover, the algorithm can obtain several topologies of SMT, which is beneficial for optimizing congestion in VLSI global routing stage. [ABSTRACT FROM AUTHOR]

There are a lot of multi-objective optimization problems (MOPs) in the real world, and many multi-objective evolutionary algorithms (MOEAs) have been presented to solve MOPs. However, obtaining non-dominated solutions that trade off convergence and diversity remains a major challenge for a MOEA. To solve this problem, this paper designs an efficient multi-objective sine cosine algorithm based on a competitive mechanism (CMOSCA). In the CMOSCA, the ranking relies on non-dominated sorting, and the crowding distance rank is utilized to choose the outstanding agents, which are employed to guide the evolution of the SCA. Furthermore, a competitive mechanism stemming from the shift-based density estimation approach is adopted to devise a new position updating operator for creating offspring agents. In each competition, two agents are randomly selected from the outstanding agents, and the winner of the competition is integrated into the position update scheme of the SCA. The performance of our proposed CMOSCA was first verified on three benchmark suites (i.e., DTLZ, WFG, and ZDT) with diversity characteristics and compared with several MOEAs. The experimental results indicated that the CMOSCA can obtain a Pareto-optimal front with better convergence and diversity. Finally, the CMOSCA was applied to deal with several engineering design problems taken from the literature, and the statistical results demonstrated that the CMOSCA is an efficient and effective approach for engineering design problems. [ABSTRACT FROM AUTHOR]

Constructing a timing-driven Steiner tree is very important in VLSI performance-driven routing stage. Meanwhile, non-Manhattan architecture is supported by several manufacturing technologies and now well appreciated in the chip manufacturing circle. However, limited progress has been reported on the non-Manhattan performance-driven routing problem. In this paper, an efficient algorithm, namely, TOST_BR_MOPSO, is presented to construct the minimum-cost spanning tree with a minimum radius for performance-driven routing in Octilinear architecture (one type of the non-Manhattan architecture) based on multi-objective particle swarm optimization (MOPSO) and Elmore delay model. Edge transformation is employed in our algorithm to make the particles have the ability to achieve the optimal solution while Union-Find partition is used to prevent the generation of invalid solution. For the purpose of reducing the number of bends which is one of the key factors of chip manufacturability, we also present an edge-vertex encoding strategy combined with edge transformation. To our best knowledge, no approach has been proposed to optimize the number of bends in the process of constructing the non-Manhattan timing-driven Steiner tree. Moreover, the theorem of Markov chain is used to prove the global convergence of our proposed algorithm. Experimental results indicate that the proposed MOPSO is worthy of being studied in the field of multi-objective optimization problems, and our algorithm has a better tradeoff between the wire length and radius of the routing tree and has achieved a better delay value. Meanwhile, combining edge transformation with the encoding strategy, the proposed algorithm can significantly reduce nearly 20 % in the number of bends. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT), real-time decision making has gradually become one of the important characteristics in mobile edge computing (MEC) environment of IoT. Therefore, in the Integrated Circuit (IC) design of MEC environment for IoT, low delay is one of the important optimization objectives. In the many routing competitions, wirelength, overflow, and runtime are the main evaluation standards, so how to reduce wirelength, overflow, and runtime has become a major challenge. However, the existing work lacks the excellent optimization ability in wirelength, overflow, and runtime, or only considers some of the optimization objectives. Therefore, we consider the wirelength, overflow, and runtime as the optimization objectives and propose a high-quality global routing algorithm of IC design in MEC environment, including the following effective strategies: (1) A hybrid topology optimization strategy combining Prim algorithm and divide-and-conquer method, (2) a heuristic search algorithm considering the congestion and the wirelength of nets. Due to the use of the Fast Lookup Table (FLUTE) algorithm to construct the topology of each net, there are too many Steiner points. For this reason, we used Prim algorithm and divide-and-conquer method to construct the topology, and thus it can avoid the problem of redundant Steiner points. In addition, we propose a congestion area identification method based on interval division to determine the area and order of nets to rip-up and reroute (R&R). Furthermore, a heuristic search algorithm that considers both the congestion and the wirelength of nets is used to optimize the total wirelength in the R&R stage. In terms of the total overflow, the total wirelength and the runtime, the experimental results show that the proposed strategies have achieved effective optimization, which can better satisfy the demand of low delay of IC design for data processing in MEC environments of IoT. [ABSTRACT FROM AUTHOR]

Clustering has long been an enduring and promising task in machine learning. However, developed one-side clustering is still insufficient to explore the context of data, such as texts and genes. Hence, developing two-way clustering has drawn more attention in recent years, which tends to cluster samples and features simultaneously. This paper proposes a sparse neighbor constrained co-clustering via category consistency learning, for alleviating the misclassification of close points. Following an additional observation, samples often fall into the same category as their neighbors, as do features. Accordingly, the co-clustering problem is formulated as nonnegative matrix tri-factorization appended dual regularizers, considering coherence between data affinity and label assignment. Then, a multiplicative alternating scheme is raised for objective optimization, whose convergence and correctness are theoretically guaranteed. Furthermore, the proposed approach is validated on six datasets using three evaluation metrics, whose parameter sensitivity is analyzed as well. Finally, comprehensive experiments show that our algorithm is competitive against existing ones. [ABSTRACT FROM AUTHOR]

In the era of the knowledge economy, one of the key issues is how to integrate and use intelligent information systems to collect data and make valuable predictions to support business decisions. Intelligent information systems use artificial intelligence to enhance system performance, giving the enterprise a competitive advantage. This paper uses ontology technology for user requirements analysis—defining the class, slot, and instance of the ontology—and then designs the system architecture based on that ontology. In accordance with the firm's requirements, we build a data warehouse system that is integrated with different data sources within the enterprise and that supports a web-based interface in the Internet of Things (IoT). The system also supports the standard queries, reports, summary tables, and datasets required for data mining. The proposed data mining method for manufacturing industries that use Bayesian network incorporates Bayesian theory and graphical models and can predict causal and probabilistic relationships among a set of variables. Our results bring information system functionality closer to satisfying the real-world needs of business. The proposed system can reduce production cycle times, increase the speed and accuracy with which production information is analyzed, and offer predictions that can be used for better business decisions. Data mining technology can improve the efficiency of manufacturing processes by using feedback data to tune the manufacturing parameters and improve the accuracy of yield rate predictions, giving the firm a greater competitive advantage. [ABSTRACT FROM AUTHOR]

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

• Form a weighted 0-norm non-negative matrix tri-factorization with global and local discriminant regularizers to simultaneously considers feature weights, data noise, local manifolds, and global scatter in co-clustering. • Optimize the formed objective function to solve the proposed co-clustering problem by an alternate update rule, whose convergence is proved in theory. • Verify our algorithm's duality, robustness, effectiveness, and competitiveness through comprehensive experiments on synthetic, corrupted and real datasets. In the past few decades, the clustering problem has made considerable progress, and co-clustering algorithms have attracted more attention. Compared with one-side clustering, co-clustering not only groups samples according to the distribution of features but also groups features according to the distribution of samples at the same time. This duality helps to explore the structural information of data, such as genes and texts. In this paper, a new co-clustering algorithm is proposed to simultaneously consider feature weights, data noise, local manifolds, and global scatter, named robust weighted co-clustering with global and local discrimination. Furthermore, an alternate update rule is put forward to optimize objective, theoretically proven to converge. Then, the algorithm's duality, robustness, and effectiveness have been verified on synthetic, corrupted, and real datasets, respectively. The runtime and parameter sensitivity of the algorithm are also analyzed. Finally, sufficient experiments clarify the competitiveness of our algorithm compared to other ones. [ABSTRACT FROM AUTHOR]

Recent advances in continuous-flow microfluidics have enabled highly integrated lab-on-a-chip biochips. These chips can execute complex biochemical applications precisely and efficiently within a tiny area, but they require a large number of control ports and the corresponding control logic to generate required pressure patterns for flow control, which, consequently, offset their advantages and prevent their wide adoption. In this article, we propose the first flow-control layer co-synthesis flow called MiniControl, for continuous-flow microfluidic biochips under strict constraints for control ports, incorporating high-level synthesis, physical design, and control system design simultaneously, which has never been considered in previous work. With the maximum number of allowed control ports specified in advance, this synthesis flow aims to generate biochip architectures with high execution efficiency and the corresponding control systems with optimized timing performance. Besides, the overall cost of a biochip can be reduced and the tradeoff between a control system and execution efficiency of biochemical applications can be evaluated for the first time. The experimental results demonstrate that MiniControl leads to high execution efficiency, low platform cost, as well as excellent timing performance, while strictly satisfying the given control-port constraints. [ABSTRACT FROM AUTHOR]

Wireless networks have been widely adopted and introduced in the areas of engineering, manufacturing, weather monitoring, transportation, etc., to collect data to improve the quality of decision making, but issues arise, such as large volumes of data, incomplete and incompatible data sets, and noise data that prevent from realizing the true value and exploiting their full potentials. Machine learning and deep learning methods have been used as powerful tools to perform feature detection/extraction and trend estimation/forecasting in wireless networks applications. Furthermore, the reinforcement learning methods, including generative adversarial networks (GANs) and deep Q-networks (DQNs) [[6]], are tools for generative networks and discriminative networks to optimize the contesting process in a zero-sum game framework. Topics covered in this issue are categorized into the following six themes: (1) networking optimization, (2) unmanned aerial vehicle (UAV) applications, (3) wireless sensor networks, (4) network security, (5) mobile positioning, and (6) image-based applications. [Extracted from the article]

• Propose an efficient convergent algorithm for high-order matrix factorization. • Construct a unified framework for feature selection and data fusion. • Present one globally structured learning regularizer via sparse representation. • Establish a new method for optimization problem with orthogonality constraints. Feature selection aims at searching the most discriminative and relevant features from high-dimensional data to improve the performance of certain learning tasks. Whereas, irrelevant or redundant features may increase the over-fitting risk of consequent learning algorithms. Structured learning of feature selection is to embed intrinsic structures of data, such as geometric structures and manifold structures, resulting in the improvement of learning performance. In this paper, three types of structured regularizers are embedded into the feature selection framework and an iterative algorithm with proved convergence for feature selection problem is proposed. First, serving as crucial representation pipelines of local structures, three types of local learning regularizers, including graph Laplacian, neighborhood preservation and sparsity regularizer, are defined. Second, the local and global structures are integrated into one joint framework for the feature selection problem. Third, the framework is formulated as the canonical form of high-order matrix factorizations and then an efficient convergent iterative algorithm is proposed for the problem. Besides, the proposed framework is further extended to multi-view feature selection and fusion problems from an algorithmic view. Finally, the proposed algorithm is tested on eight publicly available datasets and compared to several state-of-the-art feature selection methods. Experimental results demonstrate the superiority of the proposed method against the compared algorithms in terms of clustering performance. [ABSTRACT FROM AUTHOR]