Abstract: T-Gate fabrication processes for InP-based High electron mobility transistors (HEMTs) are described using PMMA/Al/UVIII. The single-step and two-step Electron beam lithography (EBL) methods are proposed contrastively without dielectric support layer. The optimal gate-foot length is 196nm for 50nm geometry path by single-step EBL technique. Since the gate-foot and gate-head are defined independently, the two-step EBL process minimizes forward scattering and enables smaller gate-foot length, which improves to be 141nm for 50nm geometry path and also 88nm for 30nm geometry path. Both EBL methods have been incorporated into InPbased HEMTs fabrication. With the gate-foot length decreases from 196nm to 141nm, the current-gain cutoff frequency (fT) is improved from 125GHz to 164GHz, and also the maximum oscillation frequency (fmax) increases from 305GHz to 375GHz.
Abstract: Parallelism in the theoretical computation mainly depends on the particular paradigm or computational environment considered, and its importance has been confirmed with the emergence of each novel computing technique. Programmable tile assembly is a novel computing technique to tackle computationally difficult problems, in which computing time grows exponentially corresponding to problematic size. The Maximum independent set (MIS) problem is a typical nondeterministic polynomial problem, which is often associated with strategy applications. In this paper, a novel approach dealing with the MIS problem is proposed based on the abstract tile assembly model, which is believed to be better than the conventional silicon-based computing in solving the same problem. The method can get the solutions of the MIS problem in θ(m+n) time complexity based on θ(mn) distinct tile types.
Abstract: Internet of things (IoT) is an emerging technique that offers advanced connectivity of devices, systems, services, and human beings. With the rapid development of hardware and network technologies, the IoT can refer to a wide variety and large number of devices, resulting in complex relationships among IoT devices. The dependencies among IoT devices, which reflect their relationships, are with reference value for the design, development and management of IoT devices. This paper proposes a stochastic model based approach for evaluating the dependencies of IoT devices. A random walk model is proposed to describe the relationships of IoT devices, and its corresponding Markov chain is obtained for dependency analysis. A framework as well as schemes and algorithms for dependency evaluation in real-world IoT are designed based on traffic measurement. Simulation experiments based on real-life data extracted from smart home environments are conducted to illustrate the efficacy of the approach.
Abstract: The paper studied the research status of fuzzy decision on the complex phenomenon. It proposed that the current researches focus on subjective fuzzy and objective randomness in language research information about this field, and the uncertainty factors influencing on the decision alternatives. To solve above problems, a new kind of fuzzy decision method was presented with cloud model and SPA. The method was used for the study of "The new rural energy development and utilization plan" to establish the renewable energy development model, and the corresponding solution was given. An active result was obtained in the experiment compared with the fuzzy twotuple linguistic analysis method, and it shows the method is rational, efficient and practical.
Abstract: This paper presents a low-power low-cost 780MHz CMOS Frequency shift keying (FSK) receiver for short-range wireless communication. A current-reused Low power amplifier (LNA) and a single-balance passive mixer are employed to cut down the current consumption of the RF frontend in receiver. A 3rd-order active-RC complex filter with reconfigurable bandwidth is implemented and a novel automatic tuning circuit is used in the filter to eliminate the negative effect of the Process, voltage, temperature (PVT) variations. A novel replica-circuit master-slave automatic tuning circuit and a discrete-time differentiator are employed in demodulator to keep the demodulation performances from the PVT variations and the frequency offset of Intermediate frequency (IF). The receiver is fabricated in 0.18μm CMOS and occupies a chip area of about 0.97 mm2. It can achieve a sensitivity of -83.7dBm and a tolerance of IF range from 1.5MHz to 3.7MHz with a 4.7mA current consumption under a 1.8V supply.
Abstract: A quantitative verification method is proposed to quantitatively verify knowledge precondition of actions and plans. Probability epistemic game structure (PEGS) is proposed to model knowledge preconditions in open systems, an extension of concurrent game structure with probabilistic transition. We introduce a probability operator P-λ into Alternating temporal epistemic logic (ATEL) and define a Probability alternating-time temporal epistemic logic (PATEL) for quantitatively model checking the properties of PEGS. We designed an algorithm to compute the probability aiming at model checking verification problems of PATEL based on DTMC and CTMC. We convert a portion of the PATEL verification problems into PATL problems by defining the knowledge formulas Kaφ, EAsφ and CAsφ as atomic propositions. We study a train controller using PRISM-games to demonstrate the applicability of above quantitative verification method.
Abstract: To improve the test automation in software development process, following the researches on test cases generation technology from models, an incremental test case generation approach is proposed based on finite automata, and Event deterministic finite automata (ETDFA) are employed to describe the sequence diagram models of system interaction. By model checked with Propositional projection temporal logic (PPTL), the correctness of ETDFA is verified. Then we can get the composed automata by synthesis rules, and generate the test cases incrementally by test cases generation algorithm. Case studies are presented to show that this approach enables to improve test cases correctness, and reduce the complexity of test cases generation process.
Abstract: We study a new influence maximization problem about how to find a seed set which can maximize the influence spread to a targeted user group in microblogging. To solve this problem, we propose a threestage User group greedy algorithm (UGGreedy) based on user attributes. To reduce network scale, we delete useless user nodes, and rank the rest of users based on user attributes to form a seed candidate set. We employ the seed candidate set to construct a simplified microblogging network graph.We propose a novel influence greedy algorithm based on influence accumulation spread to find the seed set. Experimental results show that UGGreedy can achieve remarkable efficiency on the influence maximization problem for user group in real microblogging networks.
Abstract: Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of an algorithm. The traditional Expectation maximization (EM) algorithm only produces locally optimal solutions, it is sensitive to initialization, and the number of components of mixture model must be known in advance. We propose a novel semi-supervised clustering algorithm that uses Gaussian mixture models (GMM) as the underlying clustering model. A novel adaptive global search mechanism is introduced into semi-supervised gaussian mixture model-based clustering, where the EM algorithm is incorporated with the ideas of an immune clonal selection technique. The new algorithm overcomes the various problems associated with the traditional EM algorithm. And it can improve the effectiveness in estimating the parameters and determining the optimal number of clusters automatically. The experimental results illustrate the proposed algorithm provides significantly better clustering results, when compared with other methods of incorporating equivalence constraints.
Abstract: Faced with the rapidly increasing Web services, it becomes a challenging issue for users to effectively and accurately discover and reuse services. Existing service discovery approaches are mainly developed for services with WSDL documents, while only a few attention is being paid to services described in natural language, especially to the RESTful services. Towards this issue, we introduce aWeighted service goal model (WSGM) by measuring the weights of service goals extracted from the textual descriptions of services. Based on the WSGM, a novel service discovery approach called Service discovery based onWSGM (WSGM-SD) is proposed. Experiments on ProgrammableWeb, a public Web service repository, demonstrate the effectiveness of the proposed approach.
Abstract: The classic regression model for multivariate time series prediction suffers from the curse of dimensionality because the least squares estimates become unreliable when the predictors of the time series are large and the number of samples is limited. The classic methods such as ridge regression, lasso regression, principal component regression are researched to solve the problem above. The ridge regression and principal component regression can not give the clear interpretation features for prediction, and lasso regression does not work well under collinearity, also the selections given by lasso regression are unstable and very sensitive to minor perturbations of the data. A practical method based on improved Incremental association Markov blanket (IAMB) with Expanded Markov blanket (EMB) was proposed for high-dimensional time series prediction. The EMB was constructed with simultaneous predictors and past predictors for time series. Since the faithfulness condition and reliable conditional independence test are not satisfied for high-dimensional time series with limited samples in practical applications. The symmetry of Markov blanket (MB) and the partial correlation coefficient criterion for conditional independent test were employed to learn the EMB, on which the regression was used for prediction. Empirical results show that our method based on EMB for macroeconomic prediction has less mean-square forecast error than other classic methods, especially when predicting the value with sharp fluctuation.
Abstract: The paper addresses the problem of estimation fusion for maneuvering target tracking in the presence of unknown cross-correlation. To improve the fusion accuracy, two major points are concerned. Firstly, the Interacting multiple model (IMM) estimator is performed for each sensor, and the local estimate is represented by a Gaussian mixture model instead of a Gaussian density to keep more details of the local tracker. Next, a close-formed solution of fusing two Gaussian mixtures in the Covariance intersection (CI) framework is derived to cope with the unknown cross-correlation of local estimation errors. Experimental results demonstrate that the proposed approach provides some improvements in the fusion accuracy over the competitive methods.
Abstract: Video transmission over packet-switched networks usually suffers from packet losses. The use of the prediction loop in video coding will cause these errors to propagate to subsequent frames, and thus significantly impacts on the received video quality. With the increasing number of cameras to capture the scene, robustly delivering multi-view video over error-prone channels becomes a rather challenging task. A rate-distortion optimization algorithm is proposed to improve error resilience for multi-view video transmission. A recursive model to estimate the end-to-end distortion is developed for multiview video coding, in which the distortion model explicitly takes into consideration the inherent error resilience property of the hierarchical bi-prediction structure. Based on the proposed distortion model, end-to-end rate-distortion optimization criterion is employed to perform coding mode switching. Extensive experimental results demonstrate significant performance gains can be achieved for multi-view video communication against transmission errors.
Abstract: Image transmission is one of the biggest challenges in wireless sensor networks because of the limited resource on sensor nodes. We proposed two image transmission schemes driven by reliability and real time considerations in order to transfer JPEG images over Zigbee-based sensor networks. By adding two bytes counter in the header of data packet, we can easily solve the repeated data reception problem caused by retransmission mechanism in traditional Zigbees network layer. We proposed an efficient retransmission and acknowledgment mechanism in Zigbees application layer. By classifying different data reception response events, we can provide data packets with differential responses and ensure that image packets can be transferred quickly even with large maximum number of retransmission. Practical results show the effectiveness of our solutions to make image transmission over Zigbee-based sensor networks efficient.
Abstract: This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.
Abstract: In this paper, we discuss a class of n-stage Nonlinear feedback shift registers (NFSRs). Applying the knowledge of algebra, the least periods of the sequences generated by these NFSRs with symmetric feedback functions can be characterized. This approach simplifies the problem of determining the periods of the NFSR sequences and it generalizes some Cheng's results.
Abstract: In 1982, H. Fredricksen presented the upper and lower bounds of the number of ones in the truth table of characteristic functions of de Bruijn sequences. In this paper, the distribution of ones in the truth table of characteristic functions of de Bruijn sequences is further studied. We provide the upper and lower bounds of the number of ones in the partial truth table of characteristic functions of de Bruijn sequences. Furthermore, the two bounds are tight.
Abstract: With the evolution of information communication technology, a large number of new applications appear, resulting in kinds of new accessing equipments to be deployed. Since the characteristics of randomness, dynamic and unpredictability of their positions, securely discovering these new equipments is one of a primary challenges to guarantee the security issues for these new applications. Traditional path discovery protocols cannot be directly borrowed and applied onto new service models due to the aforementioned inherent vulnerabilities. In this paper, we propose a Public key infrastructure-based (PKIbased) Trustworthy path discovery mechanism (TPDM), which is further implemented by trustworthy node discovery and neighbor node discovery protocols for new service models. As a result, a source node can discover a trustworthy path to the destination node and each intermediate node can also detect its neighbor nodes. Security analysis and evaluation results indicate the effectiveness and efficiency of our proposed mechanism.
Abstract: A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems (in short, AFSN P systems) and Particle swarm optimization (PSO) algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis eminently. Furthermore, particle swarm optimization algorithm is introduced into the learning algorithm of AFSN P systems, thus the convergence speed of diagnosis has a big progress. An example of 4-node system is given to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.
Abstract: Agglutinative language involves agglutination extensively, which results in the significant pronunciation variations in different contexts. Therefore, it is a problem to use phoneme sets translated from their written forms as basic units for acoustic modeling, due to the incapability to capture the pronunciation variations in Large-vocabulary continuous speech recognition (LVCSR). This paper presented a novel approach called Automatic allophone deriving (AAD) to create allophone candidates without any linguistic prior knowledge. Furthermore, an enhanced approach AAD-LT is proposed in which longtime features are used in AAD approach. Experiments are conducted on three languages which contains two agglutinative ones and an analytic one. The experiments suggest that AAD Long-time (AAD-LT) is very effective for the agglutinative languages in which more than 10% relative CER reduction is obtained.
Abstract: Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings sub-matrix. Compared with several major existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy.
Abstract: In this paper, adaptive Power allocation (PA) on the source and relay is investigated to maximize Energy efficiency (EE) for OFDM-based Opportunistic regenerative relay links (O2RL).We derive EE function from O2RL model by appropriate definition of variables to represent source/relay PA, Subcarrier transmission mode selection (STMS), and circuit power consumptions elegantly. Theoretical analysis demonstrates that EE function for O2RL is strictly quasi-concave with respect to the overall transmit power. Exploiting the quasi-concavity property of the EE function, an EE-oriented optimal PA iterative method for O2RL is proposed which used the bisection method to speed up the search of the optimum. Simulation results demonstrate that the O2RL is more energyefficient than Traditional cooperative relaying strategies (TCRS), and the proposed method as compared to the Sum throughput maximization PA (STMPA) method is able to significantly improve performance in terms of system EE.
Abstract: Vehicular ad-hoc network (VANET) has distinctive environments such as highly dynamic topology, frequently disconnected network, hard delay constraints for safety-related application, and various communications environments based on highway or urban traffic scenarios. Therefore, development of a suitable routing protocol that considers these characteristics of VANET should be needed. In this paper we propose the improved distance-based VANET routing protocol in urban traffic environments. We apply approaches to multi-hop broadcast scheme for reliable packet dissemination in the intersection and stable route decision scheme based on the adaptive waiting time. The performance is evaluated under simulation which is implemented with the Manhattan urban traffic model. The results show the improved performance as compared with other protocols.
Abstract: Cooperative relaying has been emerging as a key technology in Cognitive radio (CR) networks. A two-way Amplify-and-forward (AF) based multi-relay CR network is considered, where a primary user coexists with a pair of secondary users and multiple relays. A Timeefficient sub-optimal power allocation scheme (TESOPA) based on Cauchy-Schwarz inequality is provided to maximize the total end-to-end transmission rate of the secondary system. TESOPA is proposed under maximal transmission power constraints, while ensuring the quality of service of the primary user during the whole communication process. The computational complexity is sharply decreased by using the Cauchy-Schwarz inequality. Simulation results show that the proposed TESOPA scheme performs very close to the near-optimal Interior point method based power allocation (IPMPA), especially in interference dominant networks where interference constraints play a key role in determining power allocation results instead of maximal transmission power constraints.
Abstract: Modeling dynamic user behavior over online social networks not only helps us understand user behavior patterns on social networks, but also improves the performance of behavior analysis tasks. Time-varying user behavior is commonly influenced by multiple factors:user habit, social influence and external events. Existing works either consider only a part of these factors, or fail to model the dynamics behind user behavior. Thus, they cannot precisely model the user behavior. We present a generative Bayesianmodel HES to model dynamic user behavior data. We take the influential factors and user's selection process as separate latent variables, based on which we can recover the evolving patterns underneath user behavior data sequences. Empirical results on large-scale social networks show that the proposed approach outperforms existing user behavior prediction models by at least 8% w.r.t. prediction accuracy. Our work also unveils some interesting insights underneath social behavior data.
Abstract: Joint transmission is one of the major transmission schemes in Coordinated multipoint (CoMP) transmission/reception systems for Long term evolutionadvanced (LTE-A). Due to different distances between User equipments (UE) and Base stations (BS), signals are not able to arrive at the receiver with perfect synchronization, which implies that the reception at UE is asynchronous. This paper presents an evaluation on asynchronous UE reception in multi-cell downlink joint transmission systems using our LTE-based CoMP simulator. Then, due to asynchronous reception, we propose an improved reception strategy to mitigate the interference which compensate for Rx timing difference on Joint transmission (JT) CoMP systems. Simulation results show that the per-subband global precoding scheme widely used in the CoMP system is considerably sensitive to asynchronous reception since the performance is dominated by the subcarrier used for precoding vector calculation. It is verified that our proposed solution is able to achieve significant improvements under asynchronous reception.
Abstract: A novel microstrip patch antenna with both frequency and circular polarization diversities is presented. The proposed antenna consists of two parts, a patch with two orthogonal slots on the upper substrate and a 3dB hybrid coupler fed structure which printed on the lower substrate. Two groups of diodes placed across the orthogonal slots are utilized to achieve frequency reconfiguration. Another two diodes, placed between the input port of the antenna and the 3dB hybrid coupler, are utilized to switch between Left-hand circular polarization (LHCP) and Right-hand circular polarization (RHCP). Full wave simulation is used for the antenna analysis, and a prototype of the antenna with an integrated Direct current (DC) biasing circuit has been fabricated and measured. The results verify the reconfigurable characteristics of the antenna and demonstrate a good candidate for wireless communication applications.
Abstract: This paper proposes to solve the extralarge scale Electromagnetic (EM) scattering problem on a wind-driven sea surface at Low-grazing-angle (LGA) directed against a shore-based radar with an Iterative physical optics (IPO) model. Using Monte Carlo simulations, it compares the average Doppler spectra of backscattered signals at different angles and sea states. The methods are:the forward-backward methodology and its modification with under-relaxation iteration which improves convergence and stability of the IPO; the local iteration methodology and the Fast far-field approximation (FaFFA) in the matrix-vector product which reduce the computational complexity based on the scattering characteristics of sea surface. The simulations show a broadening effect of the Doppler spectra in a more complicated sea state at LGA.
Abstract: Optimality analysis of sensor to target observation geometry for bearing-only passive localization is of practical significance in engineering and military applications and this paper generalized predecessors' researches in two-dimensions into three dimensional space. Based on the principles of Cramer-Rao lower bound (CRLB), Fisher information matrix (FIM) and the determinant of FIM derived by Cauchy-Binet formula, this paper configured the optimal observation geometry resulted from maximizing the determinant of FIM. Optimal observation geometry theorems and corresponding propositions were proved for N≥2 sensors in three dimensions. One conjecture was proposed, i.e., when each range of N(N≥4) sensors to the single target is identical, configuring the optimal geometry is equivalent to distributing N points uniformly on a unit sphere, which is one of the worldwide difficult problem. Studies in this paper can provide helpful reference for passive sensor deployment, route planning of detection platform and so on.