An automatic forecasting method is proposed concerning automation problem in the field of linear time series forecasting. The method is on the basis of econometric theory and overcomes the difficulty to mine and forecast automatically with econometric models. The proposed algorithm is divided into 4 stages, i.e. preprocessing, unit root testing and stationary processing, modeling, and ultimately forecasting. Future values and trends would be estimated and forecasted precisely through the 4 stages of the algorithm according to input data without manual intervention. Experimental comparisons were made between the proposed algorithm and the 2 data driven forecasting algorithms, i.e. moving average method and Holt exponential smoothing method. It was demonstrated with the experimental results that automatic forecasting is feasible utilizing the proposed algorithm and higher accuracy can be acquired than these 2 data driven-based methods.

With the growing trend of deploying Software defined networks (SDN) in Internet and data centers, it is necessary to investigate the operation of multicast services in this paradigm shift. We propose an OpenFlowbased virtualized platform to support multiple multicast services simultaneously, each with their own customized multicast algorithms and performance-enhancing strategies for its service requirements. The contributions include: 1) systematic discussions of the technical challenges re-garding to implementing multicast services in SDN, with the proposed architectural components addressing these issues; 2) a virtualized platform enabling user-defined multicast services, independent of current Internet-based protocols; 3) experiments validating the necessity and efficiency of the platform in supporting its design goals and functionalities, with two additional real multicast applications further confirming the feasibility and operability of the proposed prototype.

Spin-torque transfer RAM (STT-RAM) is a promising candidate to replace SRAM for larger Last level cache (LLC). However, it has long write latency and high write energy which diminish the benefit of adopting STT-RAM caches. A common observation for LLC is that a large number of cache blocks have never been referenced again before they are evicted. The write operations for these blocks, which we call dead writes, can be eliminated without incurring subsequent cache misses. To address this issue, a quantitative scheme called Feedback learning based dead write termination (FLDWT) is proposed to improve energy efficiency and performance of STT-RAM based LLC. FLDWT dynamically learns the block access behavior by using data reuse distance and data access frequency, and then classifies the blocks into dead blocks and live blocks. FLDWT terminates dead write block requests and improves the estimation accuracy via feedback information. Compared with STT-RAM baseline in the lastlevel caches, experimental results show that our scheme achieves energy reduction by 44.6% and performance improvement by 12% on average with negligible overhead.

For complex multi-source, multi-product, multi-stage Supply chain network (SCN) design problem, we propose an optimization supply chain network model. We consider cash conversion cycle as an objective to this model and utilize a modified genetic algorithm to solve the problem. To describe the structure of supply chain network, we propose a new encoding method and a genetic algorithm with modified genetic operators. We use the Pareto approach to obtain the set of Pareto-optimal solutions. In order to evaluate the performance of the modified genetic algorithm and validate the model, we conduct comparisons with standard genetic algorithm and the simulated annealing genetic algorithm. Experimental results show that the modified genetic algorithm achieved better CPU time and the accuracy of the Pareto-optimal solutions than the alternative algorithms and the model was effective.

The reasons which take huge losses to enterprises and users are: Open authorization (OAuth) 2.0 protocol is excessively dependent on Hyper text transfer protocol over secure socket layer (HTTPS) to transmit data and ignores per-message encryption, and the transmission efficiency of HTTPS is too low to work well under poor network. The improved OAuth 2.0 modified by Hyper text transfer protocol (HTTP), public key system and private key signature is proposed. With verifying the security of OAuth 2.0 by model checking technology, an improved protocol of higher security is acquired. Comparing different protocol modeling optimized by three combination optimization strategies which involve technologies such as type checking, static analysis and syntactic reordering, an optimal security verification model of the improved protocol is obtained. Program enumeration is presented to compute the repository of attacker. The modeling method of attacker above can effectively reduce the complexity of attacker modeling, consequently those methods can be applied to analyze and validate multi-principal protocols.

As the development and wide usage of Software architecture (SA), SA evolution becomes one of the hotspots of current research in modern software engineering domain. Most current researches concentrate on the modeling of SA evolution and lack the verification and evaluation of SA evolution. We proposed a verificationbased approach to evaluate SA evolution. The basic process includes: 1) using Unified modeling language (UML) sequence diagram to model the interaction of components and study different types of evolution in practical examples; 2) using SPIN-based model checking to model and verify SA evolution; 3) comparing various verification outcomes and analyzing the influence of SA evolution on SA correctness and temporal properties. Both theory analysis and an experiment on a real evolution example from Model-view controller (MVC) to Spring web MVC (SWMVC) show that the verification-based approach to evaluate SA is significant.

Fragmentation usually occurs when data space of original storage nodes has to be reallocated to new added storage nodes during the scale-out evolution of the large-scale storage system. It greatly influences its performance and becomes a challenge to manage the whole space. We present an efficient space management framework, called NewBalance, to reduce fragmentation with the minimum data movement while keeping the storage system load balance. The space management framework has two phases including the collection phase and the allocation phase. For the collection phase, we propose a novel algorithm, called the greedy bi-direction collector, which collects enough space for the new storage nodes. For the allocation phase, we formally represent it as a variant of the bin packing problem and then utilize some bin packing heuristics including the first fitting and the best fitting to allocate collected intervals to new added storage nodes. The experimental results show that the amount of intervals can be reduced by 20%∼55% and our algorithmic optimization improves the data lookup performance by at least 10% and the scale-out performance by 2X∼3X.

This paper describes the design of a 5.7– 6.4GHz GaAs Heterojunction bipolar transistor (HBT) power amplifier for broadband wireless application such as wireless metropolitan area networks. A bias circuit is proposed which enhances the power gain and provides a good linearity. Using the wideband matching network techniques with trap circuits embedded to filter the harmonics and the diode-based linearizing techniques, a broadband power amplifier module was obtained which exhibited a gain above 28dB. This is about 1dB improvement compared with those normal bias circuits at a supply voltage of 5V in the frequency range of 5.7–6.4GHz, measured with Continuous wave(CW) signals. The saturated output power was greater than 33dBm in 5.7–6.4GHz and the output 1dB compression point was greater than 31dBm. The phase deviation was less than 5 degrees when the output power below 33dBm. The second and third order harmonic components were also less than −45dBc and −50dBc.

For the large driving current of high-power semiconductor Laser diodes (LDs), a modified method to measure the electrical derivative of LDs under scanning driving current with variable step length is proposed, which is to achieve the fast and accurate measurement of optical and electrical characteristic parameters of LDs with a relatively small data acquisition. The experimental results show that, with fewer measurements, this method can effectively and accurately measure and extract the LDs corresponding parameters including threshold current (I_th ), voltage-current characteristic (V-I ), luminous power-current relation (P-I ), electrical derivative curve (IdV/dI-I ). The wavelet transformation singularity testing results of the threshold current also verify the accuracy, reliability, and advantage of this method.

Cryptography circuits for portable electronic devices provide user authentication and secure data communication. These circuits should, achieve high performance, occupy small chip area, and handle several cryptographic algorithms. This paper proposes a highperformance ASIP (Application specific instruction set processor) for five standard cryptographic algorithms including both block ciphers (AES, Camellia, and ARIA) and stream ciphers (ZUC and SNOW 3G). The processor reaches ASIC-like performance such as 11.6 Gb/s for AES encryption, 16.0 Gb/s for ZUC, and 32.0 Gb/s for SNOW 3G, etc under the clock frequency of 1.0 GHz with the area consumption of 0.56 mm² (65 nm). Compared with stateof-the-art VLSI designs, our design achieves high performance, low silicon cost, low power consumption, and sufficient programmability. For its programmability, our design can offer algorithm modification when an algorithm supported is unfortunately cracked and invalid to use. The product lifetime of our design can thus be extended.

Robot path planning in uncertain dynamic environment is a hot issue in the field of Unmanned ground vehicle (UGV). Starting from the practical demands of UGV, we propose a novel dynamic obstacle avoidance algorithm based on Collision time histogram (CTH). Given current steering angle, an effective collision check model, which is called Collision check circles (CCC), is firstly calculated. The local environment information is then combined with CCC to generate the proposed CTH. The nonholonomic nature of the vehicle is embedded in this process. Finally, the proposed algorithm calculates the executing steering angle by considering both the CTH and the target point. Extensive experiments and comparisons are conducted to evaluate the performance of the proposed algorithm. Simulation experiments are firstly conducted to verify its feasibility. Furthermore, real-world experiment is conducted to verify its effectiveness. Experimental results demonstrate the practical value of the proposed algorithm.

Given the popularity of smart environment in a hospital involving the participation of a large group of patients, scheduling of patients towards their desired destination is still being a challenging issue. With the development of Internet of Things (IoT), smart environments are increasingly being deployed in various public scenarios and in particular, hospitals are an important target environment for smart environments. This paper models a dynamic scheduling policy based on a patient flow scenario in order to solve the queuing issue and facilitate people's experience. The dynamic scheduling policy aims to forward incoming patients to dissolve jams especially at the peaks by giving accurate predication of appointment time. The main modelling technique is a formal method — Performance evaluation process algebra (PEPA). The findings show that the dynamic scheduling policy is able to efficiently improve the patient flow.

Concerning on the shortcoming and complexity of Random early detection (RED) algorithm in network congestion control, a new RED algorithm based on the Hemi-Rise Cloud model (CRED) was proposed, nonlinear packet loss strategy was used, and sensitivity and uncertainty of parameters were improved. As a result, queue length could be kept stable in the neighborhood of reference value. Network congestion was well controlled and network resource was used effectively. The stability of the algorithm was studied and the experimental results showed that the proposed algorithm could improve the stability, and had better performance than the RED and Adaptive RED (ARED) algorithms.

We study skew cyclic codes over a nonchain ring, which generalizes our previous results in IEICE Trans. on Fundamentals of Electronic Communications and Computer Sciences, 2015. We describe generator polynomials of skew cyclic codes over this ring and investigate the structural properties of skew cyclic codes over the ring by a decomposition theorem. The generator polynomial of the dual code of a skew cyclic code are obtained. Moreover, the idempotent generators of skew cyclic codes are considered. Some examples are also presented to illustrate the discussed results.

Facial beauty prediction belongs to an emerging field of human perception nature and rule. Compared with other facial analysis tasks, this task has shown its challenges in pattern recognition and biometric recognition. The algorithm of presented facial beauty prediction requires burden landmark or expensive optimization procedure. We establish a larger database and present a novel method for predicting facial beauty, which is notably superior to previous work in the following aspects: 1) A largescale database with more reasonable distribution has been established and utilized in our experiments; 2) Both female and male facial beauties are analyzed under unconstrained conditions without landmark; 3) Multi-scale apparent features are learned to represent facial beauty which are more expressive and require less computation expenditure. Experimental results demonstrate the accuracy and efficiency of the presented method.

In most (t, n)-Multi-secret sharing ((t, n)MSS) schemes, an illegal participant, even without any valid share, may recover secrets when there are over t participants in secret reconstructions. To address this problem, the paper presents the notion of Group oriented (t, m, n)-multi-secret sharing (or (t, m, n)-GOMSS), in which recovering each secret requires all m (n≥m≥t) participants to have valid shares and actually participate in secret reconstruction. As an example, the paper then proposes a simple (t, m, n)-GOMSS scheme. In the scheme, every shareholder has only one share; to recover a secret, m shareholders construct a Polynomial-based randomized component (PRC) each with the share to form a tightly coupled group, which forces the secret to be recovered only with all m valid PRCs. As a result, the scheme can thwart the above illegal participant attack. The scheme is simple as well as flexible and does not depend on conventional hard problems or one way functions.

To decrease the overlarge calculation induced by the centralized processing, a new cooperative distributed Model predictive control (MPC) method is proposed for large-scale systems with coupled dynamics. Reduction and classification are investigated by defining the influence degree to reduce the whole system and then to classify the reduced system into several subsystem groups. These groups are mutually decoupled, while there is relativity between these subsystems comprised in the same group. Centralized/cooperative and distributed MPC algorithms for each group are implemented to ensure the feasibility and the stability of the whole system. Meanwhile, for practical applications, the finite times interactive control strategy between different groups is adopted to compensate information loss brought by the reduced subsystem and realize the global cooperative distributed MPC. This algorithm significantly decreases the computational load, has better control performance. Simulations are given to illustrate the effectiveness of these developed algorithms.

Binary sequences with large linear complexity have been found many applications in communication systems. We determine the linear complexity of a family of p ²-periodic binary sequences derived from poly-nomial quotients modulo an odd prime p. Results show that these sequences have high linear complexity, which means they can resist the linear attack method.

Real-time parameter identification of sinusoidal signals is an essential research topic due to its broad utilization in both theoretical studies and engineering practice. A tracking differentiator based online estimation framework has been proposed to simultaneously identify frequencies and offset of given multi-sinusoidal signal. Tracking differentiator is exploited in presented framework to track the time derivatives of measurements which are then utilized to estimate the frequencies and offset. We introduce a tracking differentiator called high-order nonlinear continuous differentiator into the framework, giving birth to a new estimation algorithm. Comparative experiments on both single and two sinusoidal signal are simulated, indicating the superiority of proposed method on both convergent speed and estimation accuracy.

Drug-drug interactions (DDIs) occur when two drugs react with each other, which may cause unexpected side effects and even death of the patient. Methods that use adverse event reports to predict unexpected DDIs are limited by two critical yet challenging issues. One is the difficulty of selecting discriminative features from numerous redundant and irrelevant adverse events for modeling. The other is the data imbalance, i.e., the drug pairs causing adverse effects are far less than those not causing adverse effects, which leads to poor accuracy of DDIs detection. We propose a multi-layer feature selection method to select discriminative adverse events and apply an over-sampling technique to make the data balanced. The experimental results show that the validation accuracy of positive DDIs on the Canada Vigilance Adverse Reaction Online Database increases to two times, and 110 DDIs are identified on the drug interactions checker of Drugs.com in USA.

In wireless sensor networks, congestion leads to buffer overflowing, and increases delay. The traditional solutions use rate adjustment to mitigate congestion, thus increasing the delay. A Delay-aware congestion control protocol (DACC) was presented to mitigate congestion and decrease delay. In order to improve the accuracy of the existing congestion detection model which is based on the buffer occupancy of a single node, DACC presents a new model considering both the real-time buffer occupancy and the average transmission time of packets. DACC uses the untapped bits in the IEEE 802.11 Distributed coordination function (DCF) frames header to carry congestion information. During the congestion alleviation period, DACC presents a channel occupancy mechanism which is based on the real-time buffer occupancy for the purpose of decreasing delay and preventing packet loss. Simulation results indicate that in terms of delay, packet delivery ratio, collision and buffer load, DACC has comparative advantages than those of 802.11 DCF, Priority-based congestion control protocol (PCCP) and Decoupling congestion control and fairness (DCCF).

Device-to-Device (D2D) communication is viewed as an emerging technology in the fifth generation systems to fully explore the proximate gain residing in local communicating pairs. But the interference imposed to the existing Cellular links (C-link) should be addressed in the optimization for underlaid D2D links (D-link). There is a performance tradeoff between Energy conservation (EC) and the number of links with satisfied Quality-of-service (QoS) requirement or System capacity (SC). We aim to weight the above tradeoff for D-links by specifying it into optimizing EC on the condition that SC is maximized and QoS requirements of C-links are fully guaranteed. A threestage joint power control and channel assignment mechanism is proposed including feasibility check, SC maximization and greedy optimization for EC. Thanks to numerical results, we illustrate the necessity to conduct our performance tradeoff and observe the gain when multiple resource variables are in elaborate cooperation.

Trust is the premise and foundation of secure communication, no matter in the quantum communication or classical communication. In fact, the existing quantum secure communication protocols and technologies are implicitly related to trust; or assume some trust premise in advance; or create or obtain a trust relationship. This paper studies on quantum trust model using node trust evaluation based on author's own research achievements. We introduce the trust management into quantum communication network to build secure trusted quantum communication network based on evaluating the trust values of nodes, which are used to evaluate the reliability of each user. We put forward a scheme of quantum trust model based on node trust evaluation, and describe the thought and process of trust evaluation in detail based on the principles of quantum entanglement and quantum teleportation. We analyze the feasibility and safety of this scheme, which provides a new thinking and method for establishing a credible secure quantum communication network.

Black-burst based multi-hop broadcast protocols are effective means to disseminate Emergency messages (EMs) in Vehicular ad hoc networks (VANETs). However, Clear to broadcast (CTB) collision will happen and reduce propagation speed. Aiming at this problem, we propose a Black-burst and multi-channel based multihop broadcast (BMMB) protocol. Vehicles with multiple antennas can transmit and sense black-burst at different channels simultaneously based on multi-channel technology. Compared with existing black-burst based methods, BMMB shortens the iterative procedure to find out the optimal segment. Only one relay vehicle can be rapidly selected within the optimal segment without CTB collision. BMMB enables alternative broadcast methods, i.e., unidirectional broadcast and multi-directional broadcast for straight roads and intersections respectively. Theoretical analysis is done for performance evaluation of BMMB, and simulation results demonstrate that BMMB performs better in terms of average one-hop delay and propagation speed.

Due to the inefficiency of traditional fixed spectrum allocation policies, the paradox of apparent spectrum scarcity occurs while most of the bands are underutilized. This has prompted proposals for Dynamic spectrum sharing (DSS), which explains why Cognitive radio network (CRN) has been widely accepted as a promising approach to settle inefficient usage of scarce available radio spectrum. As a subset of DSS, Dynamic spectrum leasing (DSL) strategy has been proposed based on game idea, where Primary user (PU) has an incentive to allow Cognitive users (CUs) to access its licensed spectrum for a fraction of time in exchange for revenue. This paper proposes an approach, named multiple relay selection based on Game theory (GTMRS), to optimize the utilities of PU and CUs as a whole, where a pricing-based spectrum leasing mechanism is applied. While the parameter price c is jointly determined by PU and CUs, all selected cognitive user's optimal cooperative powers can be satisfied through a non-cooperative game among themselves. Numerical results show that more CUs are involved in the cooperation and both utilities of PU and CUs as a whole are improved, which means the whole system throughput is increased.

This paper proposes a novel carrier Phase noise (PN) pre-correction scheme with an adaptive PN prediction algorithm for Single-carrier Frequency-division multiple-access (SC-FDMA) systems to alleviate degradation due to the PN. Our proposed PN prediction algorithm is a modified polynomial fitting algorithm which is based on receding horizon principle. The parameters of the prediction model are optimized by using the algorithm on PN samples of the local oscillator signal in a training window. By using the optimized prediction model parameters and the latest PN samples, we can predict future PN samples. Then these predicted PN samples are put into our proposed PN pre-correction scheme and the SC-FDMA symbols at the transmitter are pre-compensated. Due to the absence of the radio frequency delay device, the proposed scheme has a low hardware complexity. Simulation results show that our proposed scheme can greatly reduce the effect of the PN on the transmitted SC-FDMA signal.

Ciphertext policy attribute-based encryption (CP-ABE) is becoming a new primitive for finegrained access control. It neither produces multiple encrypted copies of the same data nor suffers from the severe burden of key distribution and management. The escrow problem that the central authority could decrypt any ciphertexts addressed to all the specific users is still a challenge for CP-ABE mechanism. One new CP-ABE scheme without escrow is proposed, and furthermore the proposed scheme achieves fully security in the standard model. The performance and security analysis results indicate that the proposed CP-ABE scheme is extremely appropriate for cloud storage system.

A reliable target detection method for Synthetic aperture radar (SAR) images is needed urgently with the wide application of SAR systems. The performance of conventional detection algorithms, such as Constant false alarm rate (CFAR), degrade significantly in low SCR or complex regions while the Human visual system (HVS) can identify targets of interest without knowing characteristics of the background even in complicated environment. The combination of HVS with the SAR-ATR system may effectively achieve real-time multi-target detection in complex occlusion scenes. A new effective target detection algorithm is put forward using hierarchical characteristics of targets. Inspired by different roles of the retina and visual cortex in the HVS, this detection algorithm is divided into coarse detection and fine detection stage. Two kinds of features based on the correlation between target features and suspicious targets, namely overall feature and refined feature, are used in these two stages respectively to extract real targets. Experimental results verify its correctness and effectiveness in complex environment.

Co/Pt multilayer dots arrays with 580-nm periodicity were fabricated using Laser interference lithography (LIL) and an ion-beam milling technique. We obtained uniform dots arrays with a large area by optimizing the exposure parameters and improving exposure conditions. We obtained their shapes and magnetic domain structures and investigated using an Atomic force microscope (AFM) and Magnetic force microscope (MFM). The AFM and MFM images show: 1) when their size is smaller than 400nm, the dots are in single-domain state and their shape tends to be circular; and 2) when their size is greater than 400nm, the dots are in multi-domain state and they tend to form squares. The dots arrays possibly can be used in nano-scale magnetic storage.

This paper proposes a modified signal processing structure based on the fractionally Nyquist sample spaced structure for passive bistatic radar. To recover the direct signal from the multipath clutter, an equalizer of the Auto regressive moving average (ARMA) type is proposed based on the fractionally Nyquist sample spaced constant modulus algorithm. Compared with the conventional Nyquist sample spaced equalizer, the equalizer of the fractionally Nyquist sample spaced ARMA structure is more effective in dealing with deep fading multipath channels with zeros near the unit circle. Computer simulations and real data tests indicate that the proposed approach outperforms the conventional processing structure in terms of both clutter residual and mean square error.