Channel estimation is one of the key technologies in mobile communications. Channel estimation is critical in providing high data rate services and to overcome fast fading in very high-speed mobile communications. This paper presents a novel channel estimation based on hybrid spreading of I and Q signals (CEHS). Simulation results show that it can effectively mitigate the influence of fast fading and enable to provide high data rates for very high speed mobile systems.

This paper proposes a component ranking method to identify important components which have great impact on the system reliability. This method, which is opposite to an existing method, believes components which frequently invoke other components have more impact than others and employs component invocation structures and invocation frequencies for making important component ranking. It can strongly support for improving the reliability of software systems, especially large-scale systems. Extensive experiments are provided to validate this method and draw performance comparison.

In order to reduce the convergence time in an iterative procedure, some gradient based preliminary processes are employed to eliminate outliers. The adaptive variable block size is also introduced to balance the accuracy and computational complexity. Moreover, the use of Canberra distance instead of Euclidean distance illustrates higher performance in measuring motion similarity.

In order to estimate fast time-varying channels exactly, the Inter-Carrier Interference (ICI) caused by time-varying fading channels in Orthogonal Frequency Division Multiplexing (OFDM) systems is analyzed based on the Basis Expansion Model (BEM). A channel estimation and ICI cancellation algorithm with low complexity is proposed. A special pilot sequence is designed to minimize the cost of computing the channel state information in the proposed algorithm. Based on the property of channel frequency impulse matrix, the ICI can be canceled iteratively in frequency domain. The complexity of the algorithm is analyzed theoretically. Through simulation, the algorithm is shown to be effective in estimating channel state information and in cancelling ICI.

A method is proposed for estimating code and carrier phase parameters of GNSS reflected signals in low SNR (signal-to-noise ratio) environments. Simulation results show that the multipath impact on code and carrier with 0.022 C/A chips delay can be estimated in 0 dB SNR in the condition of 46 MHz sampling rate.

Cloud computing has attracted a growing number of enterprises to move their business to the cloud because of the associated operational and cost benefits. Improving availability is one of the major concerns of cloud application owners because modern applications generally comprise a large number of components and failures are common at scale. Fault tolerance enables an application to continue operating properly when failure occurs, but fault tolerance strategy is typically employed for the most important components because of financial concerns. Therefore, identifying important components has become a critical research issue. To address this problem, we propose a failure-sensitive structure-based component ranking approach (FSCRank), which integrates component failure impact and application structure information into component importance evaluation. An iterative ranking algorithm is developed according to the structural characteristics of cloud applications. The experimental results show that FSCRank outperforms the other two structure-based ranking algorithms for cloud applications. In addition, factors that affect application availability optimization are analyzed and summarized. The experimental results suggest that the availability of cloud applications can be greatly improved by implementing fault tolerance strategy for the important components identified by FSCRank.

Multiple-input multiple- output (MIMO) and orthogonal frequency division multiplexing (OFDM) are two key techniques for broadband wireless mobile communications and channel state information (CSI) is critical for the realization and performance of MIMO-OFDM systems in doubly-selective fading channels. Channel estimation based on two-dimensional discrete-time Fourier transform interpolation (2D-DFTI) is a promising solution to obtain accurate CSI for MIMO-OFDM systems in theory because of both its robustness and high computational efficiency, however, its performance will degrade significantly in practical MIMO-OFDM systems due to the two-dimensional Gibbs phenomenon caused by virtual subcarriers and burst transmission. In this paper, we propose a novel channel estimation method based on the two-dimensional enhanced DFT interpolation (2D-EDFTI), i.e., the frequency-domain EDFTI (FD-EDFTI) concatenated with the time-domain EDFTI (TD-EDFTI), for practical burst-mode MIMO-OFDM systems with virtual subcarriers, which can increase the channel estimation accuracy effectively by mitigating the Gibbs phenomenon in frequency-domain and time-domain, respectively, while keeping good robustness and high computational efficiency. In addition to computer simulations, we further implement the 2D-EDFTI channel estimator into our real-time FPGA testbed of 44 MIMO-OFDM transmission via spatial multiplexing, together with different MIMO detectors. Both computer simulations and RF experiments demonstrate the superior performance of 2D-EDFTI channel estimation in doubly-selective fading channels, therefore, high-throughput MIMO-OFDM transmission based on different MIMO detection algorithms can always be well supported. Also, it can be applied to other MIMO-OFDM transmission schemes straightforwardly.

In this paper, an automated computer-aided-detection scheme is proposed to identify and locate the suspicious masses in the abnormal breasts from the full mammograms. Mammograms are examined using a four-stage detection method including pre-processing, identification of local maxima, seeded region-growing, and false positive (FP) reduction. This method has been applied to the entire Mammographic Image Analysis Society (MIAS) database of 322 digitized mammograms containing 59 biopsy-proven masses in 56 images. Results of detection show 95% true positive (TP) fraction at 1.9 FPs per image for the 56 images and 1.3 FPs per image for the entire database.

Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MI-MBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms.

The traffic load of cellular networks varies in both time and spatial domains, causing many base stations (BS) to be under-utilized. Assisted by cell zooming, dynamic BS sleep control is considered as an effective way to improve energy efficiency during low traffic hours. Therefore, how densely the BSs should be deployed with cell zooming and BS sleeping is an important issue. In this paper, we explore the energy-optimal cellular network planning problem with dynamic BS sleeping and cell zooming for the cases in which traffic is uniformly distributed in space but time-varying. To guarantee the quality of multi-class services, an approximation method based on Erlang formula is proposed. Extensive simulations under our predefined scenarios show that about half of energy consumption can be saved through dynamic BS sleeping and power control. Surprisingly, the energy-optimal BS density we obtained is larger than the one without considering BS sleeping. In other words, deploying more BSs may help to save energy if dynamic BS sleeping is executed.

A new construction of shift sequences is proposed under the condition of P|L, and then the inter-group complementary (IGC) sequence sets are constructed based on the shift sequence. By adjusting the parameter q, two or three IGC sequence sets can be obtained. Compared with previous methods, the proposed construction can provide more sequence sets for both synchronous and asynchronous code-division multiple access communication systems.

Two adaptive energy detectors are proposed for cognitive radio systems to detect the primary users. Unlike the conventional energy detector (CED) where a decision is made after receiving all samples, our detectors make a decision with the sequential arrival of samples. Hence, the sample size of the proposed detectors is adaptive. Simulation results show that for a desired performance, the average sample size of the proposed detectors is much less than that of the CED. Therefore, they are more agile than the CED.

This paper presents a non-crossover and multi-mutation based genetic algorithm (NMGA) for the Flexible Job-shop Scheduling problem (FJSP) with the criterion to minimize the maximum completion time (makespan). Aiming at the characteristics of FJSP, three mutation operators based on operation sequence coding and machine assignment coding are proposed: flip, slide, and swap. Meanwhile, the NMGA framework, coding scheme, as well as the decoding algorithm are also specially designed for the FJSP. In the framework, recombination operator crossover is not included and a special selection strategy is employed. Computational results based on a set of representative benchmark problems were provided. The evidence indicates that the proposed algorithm is superior to several recently published genetic algorithms in terms of solution quality and convergence ability.

With the development of blockchain technology, the automatic generation of smart contract has become a hot research topic. The existing smart contract automatic generation technology still has improvement spaces in complex process, third-party specialized tools required, specific the compatibility of code and running environment. In this paper, we propose an automatic smart contract generation method, which is domain-oriented and configuration-based. It is designed and implemented with the application scenarios of government service. The process of configuration, public state database definition, code generation and formal verification are included. In the Hyperledger Fabric environment, the applicability of the generated smart contract code is verified. Furthermore, its quality and security are formally verified with the help of third-party testing tools. The experimental results show that the quality and security of the generated smart contract code meet the expect standards. The automatic smart contract generation will “elegantly” be applied on the work of anti-disclosure, privacy protection, and prophecy processing in government service. To effectively enable develop “programmable government”.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server; the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively studied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum unavailability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of proposed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the proposed model reduces the maximum unavailability by an average of 29% in our examined cases.

This letter is devoted to constructing new Type-II Z-complementary pairs (ZCPs). A ZCP of length N with ZCZ width Z is referred to in short by the designation (N, Z)-ZCP. Inspired by existing works of ZCPs, systematic constructions of (2N+3, N+2)-ZCPs and (4N+4, 7/2N+4)-ZCPs are proposed by appropriately inserting elements into concatenated GCPs. The odd-length binary Z-complementary pairs (OB-ZCPs) are Z-optimal. Furthermore, the proposed construction can generate even-length binary Z-complementary pairs (EB-ZCPs) with ZCZ ratio (i.e. ZCZ width over the sequence length) of 7/8. It turns out that the PMEPR of resultant EB-ZCPs are upper bounded by 4.