This article presents a power-delay-optimized monotonic-specific successive approximation register (SAR) ADC. The SAR feedback loop, comprising the proposed unbalanced N/P-MOS sizing technique, simultaneously reduces the SAR logic delay and the power to overcome the SAR ADC’s speed bottleneck. Benefiting from this technique, the sampling rate of the prototype 10b single channel 1b/cycle SAR ADC reaches 600 and 700 MS/s at 0.9 and 0.95 V supply voltage, while consuming 1.49 and 2.02 mW in 28 nm CMOS, respectively. Moreover, the 10b ADC achieves the SNDR of 56.39 and 56.42-dB at a Nyquist rate input frequency of 600 and 700 MS/s, leading to a Walden FoM of 4.6 and 5.3 fJ/conversion-step, respectively.

Service composition is an important mean for integrating the individual Web services to create new valueadded systems that satisfy complex requirements.Therefore, how to effectively analyze different types of services and find out the matching similarity between services to efficiently substitute failed services in a distributed and dynamic environment becomes crucial to service composition.In this paper, we propose a novel approach based on a data cell evolution model (DCEM) to support the dynamic adaptation of service compositions.The model combines data service information and biological cell behavior analysis to encapsulate data services into data cells.In order to reach optimum adaptations, we analyzed the static and dynamic structure of data cells based on bigraph theory to guarantee the consistency of service evolution.To evaluate the proposed approach, a series of simulation experiments and comparisons are conducted to demonstrate the effectiveness of service composition.

Time series forecasting is emerging as one of the most important branches of big data analysis. However, traditional time series forecasting models can not effectively extract good enough sequence data features and often result in poor forecasting accuracy. In this paper, a novel time series forecasting model, named SeriesNet, which can fully learn features of time series data in different interval lengths. The SeriesNet consists of two networks. The LSTM network aims to learn holistic features and to reduce dimensionality of multi-conditional data, and the dilated causal convolution network aims to learn different time interval. This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals. Moreover, this model adopts residual learning and batch normalization to improve generalization. Experimental results show our model has higher forecasting accuracy and has greater stableness on several typical time series data sets.

Automatic data clustering, whose goal is to recover the proper number of clusters as well as appropriate partitioning of data sets, is a fundamental yet challenging problem in unsupervised learning. In this paper, adaptive multisubpopulation competition (AMC) and multiniche crowding are proposed and incorporated into a memetic algorithm to tackle the problem. The AMC mechanism is developed to ensure a diverse search over solution subspaces corresponding to different numbers of clusters while allowing more promising subspaces to be more intensively searched. In this mechanism, the amount of individuals to be migrated between subpopulations is adaptively controlled according to the performance of subpopulations as well as the diversity of cluster numbers in population. Further, the migration is restricted to occur between subpopulations with relatively similar performances. Additionally, subpopulations with different performances are devised to search their corresponding subspaces with different exploration powers. The adaptive multiniche crowding scheme is designed to promote a diverse search of the subspace while allowing an efficient convergence of the corresponding subpopulation. This is achieved by dynamically adjusting parameter values of a multiniche crowding method to form and maintain diverged niches of high fitness within the subpopulation. The performance of proposed algorithm has been demonstrated through a series of experiments on both artificial and real data, and compared with existing methods. The results reveal that our proposed algorithm can achieve superior clustering performance and outperform related methods.

Recently, cloud computing has been widely studied and applied in commercial area. However, it is still in exploratory stage in government area. One main reason is that government area usually involves complex and flexible business process and business data, which requires high flexible systems. To handle these problems, this paper proposes a domain-oriented government cloud platform based on PaaS, called DOGCP. The DOGCP can integrate and manage domain-oriented software resources, totally encapsulated as services. Government departments can customize applications by utilizing these services according to their special demands. The DOGCP has been applied in the domain of qualification promotion to build various applications for departments that review different qualifications. Actual experiences show that the DOGCP has great flexibility to satisfy various demands of departments.

Individuals in large groups respond to the movements and positions of their neighbors by following a set of interaction rules. These rules are central to understanding the mechanisms of collective motion. However, whether individuals actually use these rules to guide their movements remains untested. Here we show that the real-time movements of individual fish can be directly predicted from their neighbors’ motion. We train a recurrent neural network to predict the trajectories of individual fish from input signals. The inputs are projected to the recurrent network as time series representing the movements and positions of neighboring fish. By comparing the data output from the model with the target fish’s trajectory, we provide direct evidence that individuals guide their movements via interaction rules. Because the error between the model output and actual trajectory changes when the fish perceive a noxious contaminant, the model is potentially applicable to water quality monitoring.

Storing biometric templates and/or encryption keys, as adopted in traditional biometrics-based authentication methods, has raised a matter of serious concern. To address such a concern, biometric key generation, which derives encryption keys directly from statistical features of biometric data, has emerged to be a promising approach. Existing methods of this approach, however, are generally unable to appropriately model user variations, making them difficult to produce consistent and discriminative keys of high entropy for authentication purposes. This paper develops a semisupervised clustering scheme, which is optimized through a niching memetic algorithm, to effectively and simultaneously model both intra- and interuser variations. The developed scheme is employed to model the user variations on both single features and feature subsets with the purpose of recovering a large number of consistent and discriminative feature elements for key generation. Moreover, the scheme is designed to output a large number of clusters, thus further assisting in producing long while consistent and discriminative keys. Based on this scheme, a biometric key generation method is finally proposed. The performance of the proposed method has been evaluated on the biometric modality of handwritten signatures and compared with existing methods. The results show that our method can deliver consistent and discriminative keys of high entropy, outperforming-related methods.

Clustering is deemed one of the most difficult and challenging problems in machine learning. In this paper, we propose a multilocal search and adaptive niching-based genetic algorithm with a consensus criterion for automatic data clustering. The proposed algorithm employs three local searches of different features in a sophisticated manner to efficiently exploit the decision space. Furthermore, we develop an adaptive niching method, which can dynamically adjust its parameter value depending on the problem instance as well as the search progress, and incorporate it into the proposed algorithm. The adaptation strategy is based on a newly devised population diversity index, which can be used to promote both genetic diversity and fitness. Consequently, diverged niches of high fitness can be formed and maintained in the population, making the approach well-suited to effective exploration of the complex decision space of clustering problems. The resulting algorithm has been used to optimize a consensus clustering criterion, which is suggested with the purpose of achieving reliable solutions. To evaluate the proposed algorithm, we have conducted a series of experiments on both synthetic and real data and compared it with other reported methods. The results show that our proposed algorithm can achieve superior performance, outperforming related methods.

In slap fingerprint segmentation, crease is the most difficult edge to correctly detect. In this paper, we present a novel yet simple and accurate algorithm for the principal axis and crease detection. Firstly, the principal axis of each foreground region is detected using the minimal rotational inertia; Secondly, the crease detection is done based on cost function minimization. This algorithm has been incorporated in a slap fingerprint segmentation scheme, previously developed by the authors, producing successful results.

Presented here is a highly accurate and computationally efficient algorithm suitable for slap fingerprint segmentation. The main advantages of this algorithm are as follows: 1)three-order cumulant is used to roughly segment the foreground; 2)frequency domain analysis is carried out in local areas to do binarization and fine segmentation; 3)cumulative sum analysis is applied to extract the knuckle lines; 4)two shape features of the ellipse are adapted to calculate the confidence of each fingertip candidate. Experimental results show that the algorithm has the characteristic of more robustness against noise and superior precision, not only for live-scan four finger slaps but also for ten-print-card five finger slaps.

Semi-similarity design is a reasonable and effective approach for motorcycle-hydraulic-disk brake design, which is an indeterminate problem because of incomplete required parameters. First, equation of semi-similar level scale is set up through analyzing the design model of motorcycle-hydraulic-disk brake. Second, algorithm of semi-similarity design and a fuzzy evaluation model for design scheme are proposed. Third, work flowchart of semi-similarity design based on the above strategy is given and a software system of motorcycle-hydraulic-disk brake semi-similarity design is developed. Finally, the proposed strategy and method are effectively demonstrated in an instance of motorcycle-hydraulic-disk brake semi-similarity design.