Unmanned aerial vehicles (UAVs) are products of deep integration of aviation technology and Information technology (IT). The core factor of why the UAV industry can become a relatively independent industry and experience rapid development is the full penetration of IT into the aviation industry. Under the networked environment, UAVs are now becoming datadriven mobile agents. With the development of information technology, information transmission networking, operation space digitizing, and flight platform intelligentizing will become the main trends of technological development of UAV systems. The application of UAV systems is not merely a paradigm in transportation, but will more importantly produce "UAV+" novel production modes, service formats, and combat modes associated with UAVs. These emerging trends will have profound impacts on future economic and social development as well as the building of armed forces.

Increasing attention is being devoted to network security with the extensive application of wireless sensor networks in various fields. Connectivity and resilience are the crucial metrics of network security. We establish precise formulas for obtaining the resilience of a Key predistribution scheme (KPS) obtained from an Orthogonal array (OA). We study the connectivity and resilience of the Broadcast-enhanced key predistribution scheme (BEKPS) based on OAs and improve the existing methods. We also present precise formulas for evaluating the resilience of these schemes based on any number of partitions, completely solving the problem regarding the resilience of this type of BEKPS. We obtain precise formulas for ensuring the connectivity of two partitions under the condition that each pair of trees from different partitions intersects at exactly one node. We present a general approximate formula for evaluating the connectivity of more than two partitions, providing a positive solution to the open problem in Kendall et al.'s paper in ACM Transactions on Sensor Networks (Vol.11, No.1, 2014). Thus, we can conclude that for this type of BEKPS, connectivity increases, whereas resilience remains unchanged as the number of partitions increases. A comparison of the obtained results and the previously reported results reveals the superior performance of the proposed BEKPSs.

The absolute and sum-of-squares indicators are used to evaluate the Global avalanche characteristics (GAC) of Boolean functions in a global manner. The GAC properties of a class of highly nonlinear 1-resilient Boolean functions are given. We derive new upper bounds of the absolute and sumofsquares indicators for a class of 1-resilient Boolean functions with high nonlinearity. Compared to the known 1-resilient Boolean functions, the constructed functions possess higher nonlinearity and better GAC properties.

Qutrit is the natural extension of qubit in quantum information processing and has quite a few advantages that outperform qubit. In this paper, we investigate the feasibility of teleportation of an unknown qubit state, as well as an unknown qutrit state using a two-qutrit entangled pair. We show that by carefully constructing the measurement bases, both the qubit and the qutrit can be faithfully teleported from Alice to Bob with a two-qutrit maximally entangled state.

Text similarity measurements are the basis for measuring the degree of matching between two or more texts. Traditional large-scale similarity detection methods based on a digital fingerprint have the advantage of high detection speed, which are only suitable for accurate detection. We propose a method of Chinese text similarity measurement based on feature phrase semantics. Natural language processing (NLP) technology is used to pre-process text and extract the keywords by the Term frequency-Inverse document frequency (TF-IDF) model and further screen out the feature words. We get the exact meaning of a word and semantic similarities between words and a HowNet semantic dictionary. We substitute concepts to get the feature phrases and generate a semantic fingerprint and calculate similarity. The experimental results indicate that the method proposed is superior in similarity detection in terms of its accuracy rate, recall rate, and F-value to the traditional and digital fingerprinting method.

With the emergence of Internet of things (IoT), sensor hubs, which integrate data from different sensors, play increasingly important role. Energy efficiency is one of the most important issues for sensor hubs. To attack this challenge, this paper proposes a task scheduling scheme for sensor hubs to improve their energy efficiency. A multi-queue-based framework is designed, and its theoretical model and the corresponding mathematical analyses are presented. By optimizing the model with Lyapunov optimization techniques, an algorithm of energy efficient task scheduling in sensor hubs is proposed. Finally, simulation experiments based on real-life data extracted from Internet of vehicles (IoV) environments are conducted to validate the efficacy of the approach.

Text classification is a fundamental task in Nature language process (NLP) application. Most existing research work relied on either explicate or implicit text representation to settle this kind of problems, while these techniques work well for sentence and can not simply apply to short text because of its shortness and sparseness feature. Given these facts that obtaining the simple word vector feature and ignoring the important feature by utilizing the traditional multi-size filter Convolution neural network (CNN) during the course of text classification task, we offer a kind of short text classification model by CNN, which can obtain the abundant text feature by adopting none linear sliding method and N-gram language model, and picks out the key features by using the concentration mechanism, in addition employing the pooling operation can preserve the text features at the most certain as far as possible. The experiment shows that this method we offered, comparing the traditional machine learning algorithm and convolutional neural network, can markedly improve the classification result during the short text classification.

The weighted traversal pattern is important in software system for a better understanding of the internal structure and behavior of software. To mine important patterns of software, a complex network-based Optimal Software Fault Patterns Miner is presented. By analyzing the multiple execution traces of software and the relations among functions, we establish the Weighted Software Execution Dependency Graph model ultimately. The traversal database is generated through depth-first search strategy and the extraction of software path traversals. According to the downward-closure property, a pruning strategy is adopted by Weighted Frequent Candidate Pattern Tree to cut off more unpromising patterns in advance. A set of important patterns is derived without repeated calculation. The experimental results show that the proposed approach has good performance in the number of weighted frequent candidate patterns and time efficiency.

Erhai Lake is the seventh largest freshwater lake in China where has been encountered with water pollution by the algae. One of important indicator of the water quality is the density of algae in the water, and the growth rate of algae is eutrophication. A novel method is proposed to discover the association of algae with physicochemical variables related to the algae growth in Erhai Lake, by integrating Maximal information coefficient(MIC) and association rules. To measure the correlation between variables, a new aspect on association rules with non-occurring domain is purposed, which decreases one item in antecedent. The correlation results present significantly in the density levels of algae, which is beneficial to control the level of physicochemical in the water for preventing the increasing number of algae.

With the rapid development of cloud storage, an increasing number of data owners prefer to outsource their data to the cloud server, which can greatly reduce the local storage overhead. Because different cloud service providers offer distinct quality of data storage service, e.g., security, reliability, access speed and prices, cloud data transfer has become a fundamental requirement of the data owner to change the cloud service providers. Hence, how to securely migrate the data from one cloud to another and permanently delete the transferred data from the original cloud becomes a primary concern of data owners. To solve this problem, we construct a new counting Bloom filter-based scheme in this paper. The proposed scheme not only can achieve secure data transfer but also can realize permanent data deletion. Additionally, the proposed scheme can satisfy the public verifiability without requiring any trusted third party. Finally, we also develop a simulation implementation that demonstrates the practicality and efficiency of our proposal.

High-speed router design for network on chip (HSRDN) is proposed for controlling the traffic congestion and deadlocks. Diagonal based nearest-path routing algorithm for NoC (DNRAN) can mitigate the effect of latency by opting for the nearest-path to reach the destination in a network and HSRDN is part of DNRAN. When we analyze the performance of DNRAN for all proposed topologies, nearly 50% better in terms of latency reduction and high throughput over existing router architectures. The proposed topologies (2D-mesh, 2D-Star mesh over regional mesh (SMoRM), 3D-mesh, and 3D-torus) are tested with various applications, viz, audio, video and so on. Here, we also tested with cryptography application for DNRAN. When we analyzed the performance of experimental results, exclusively in 2D-SMoRM nearly 0.6 times latency get reduced, area expanded by 0.25 and 0.33 times throughput increase in 2D-SMoRM compared with 3D-mesh and 3D-torus. Therefore, DNRAN showed an exclusive performance in 2D-SMoRM compared with other two topologies.

A hybrid Analog to digital converter (ADC) is presented for long-wave infrared focal plane arrays. A two-stage quantization structure is applied in the folding integration process, which results in better chargehandling capacity and higher linearity compared with conventional designs while using fewer transistors at the pixel level. By employing a circular-adder-based counting structure with 3T dynamic memory cells, hardware consumption can be reduced. A pixel circuit of pitch 15μm has been designed using the 0.18mm Complementary metal-oxide-semiconductor (CMOS) process. The power consumption of the pixel-level ADC is 0.214μW, and the charge-handling capacity is 1Ge^-. Simulation results demonstrate a signal-to-noise ratio of 90dB and a nonlinearity of 0.11%.

As the abstraction and equivalent technologies, simulation and bisimulation have been applied to the simplifications of some classical and uncertain models structures. The studies of the more generalized simulation and bisimulation technologies have not proceeded yet. With this problem in mind, we introduce the concepts, lemmas, theorems of nondeterministic fuzzy simulation and bisimulation, as well as the relevant proofs. According to the definitions of nondeterministic fuzzy simulation and bisimulation, we propose nondeterministic fuzzy quotients and a series of quotienting algorithms to generate the minimization of nondeterministic fuzzy simulation and bisimulation. By comparison with previous quotienting algorithms, we show that our quotienting algorithms are more generalized. This kind of quotienting algorithms not only suit for Nondeterministic fuzzy Kripke structure(NFKS), but also Fuzzy Kripke structure(FKS) and classical Kripke structure.

Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined to represent a new model for DES. Each transition in DES can be described as a clause. According to CNF-FSM, we construct a CNF-diagnoser. Based on the resolution principle and CNF-diagnoser, an algorithm is presented to test whether the failure events can be detected or not in a finite number of observable events. Our algorithm can be applied in both off-line diagnosis and on-line diagnosis. Experimental results show that our algorithm can solve the diagnosability problem efficiently.

Convolutionneural network (CNN) has significantly pushed forward machine vision, which has achieved very significant results in face recognition, image classification and objection detection, and provides a new method for facial beauty prediction (FBP). Although the approach is widely applied in FBP, the research progress in FBP is relatively slow compared with face recognition. The first one is that there is less public database for FBP, and experiments for FBP are tested on small-scale database. The second one is that evaluation of facial beauty is subjective and lack of criterion,and CNN model is hard to train. In view of the problems of FBP, we expand Largescale database of Asian women's face database (LSAFBD) with data augmentation. A lighted deep convolution neural network (LDCNN) for FBP including 5650K parameters is constructed by both Inception model of GoogleNet and Max-Feature-Max activation layer, which can extract multi-scale features of an image, get compacted presentation and reduce parameters. Experiments on LSAFBD show that our LDCNN model has advantages of simple structure, small-scale parameters and is suitable for small embedded devices, with the best classification accuracy of 63.5%, which outperforms the other published CNN models for FBP.

We illustrate the principle of Digital satellite TV differential timing (DSTVDT) and propose an optimal weighting method that reduces the timing error introduced by satellite ephemeris error. A mathematical formula for the timing error introduced by satellite ephemeris error is derived and analyzed. On the basis of the derived formula, the proposed optimal weighting method based on multi-base stations is presented. The results of data simulation and experiments show that when the proposed method is applied, for a satellite ephemeris error of 10km, the ephemeris-introduced timing error is less than 10ns, which is important for improving accuracy and stability of the timing system.

To use the Automatic identification system (AIS) as a land-based positioning system for coastal vessels is a leadingedge research field. The timestamp detection method used in the AIS is quite different from the one used in general positioning system. We researches the transmission model of AIS signal over sea surface. The influence of different sea surface conditions on the propagation losses of AIS signal was analyzed. Based on the relationships between different meteorological conditions and the propagation losses, the relationship between the propagation losses and the performance of timestamp detection, an adaptive method is proposed to be used in the AIS real-time signal detection. The proposed method can adaptively determine whether to conduct the noise resistance procedure or not according to the sea surface conditions. The experimental results indicate that the timestamps can be detected precisely while the processing time is reduced about 67%. The proposed method is valid under different conditions and achieves a good performance in the field test.

To efficiently handle high-dimensional continuous optimization problems, a Modified tree-seed algorithm(MTSA) is proposed by coupling a newly introduced control parameter named as Seed domain shrinkable coefficient(SDSC) and Local reinforcement strategy(LRS) based on gradient information of adjacentgeneration best trees. SDSC is dynamically decreased with iterations to adjust the produced domain of offspring seeds, for achieving the tradeoff between the global exploration and local exploitation. LRS strategy is to execute local exploitation process by employing gradient information of two best trees, for enhancing convergence efficiency and local optima avoidance with probabilities. The compared experimental results show the different effects of differenttype SDSC on MTSA, the faster convergence efficiency and the stronger robustness of the proposed MTSA.

We propose a novel Fast dimensionreducing ranked query method (FDRQM) with high security for encrypted cloud data.We use Principal component analysis (PCA) algorithm to improve the speed of data encryption and search efficiency. Moreover, a random threshold of accumulated contribution rate of principal components is set to realize the randomness of data dimension reduction and improve data security further. Besides, we introduce a unit matrix before dimension reduction of index, which can not only improve the security of the system, but also ensure the accuracy of query. We demonstrate that our algorithm is more effective and efficient than existing algorithms.

The IEEE 802.11ax amendment for the next generation of Wireless local area network (WLAN) provides the Orthogonal frequency division multiple access (OFDMA) mechanism. The Uplink OFDMA-based random access (UORA) supports multiple users to transmit uplink data simultaneously in different Resource units (RUs). With UORA, Access point (AP) uses trigger frame to dynamically announce eligible Random access RUs (RA-RUs) for the associated mobile stations (STAs), and a STA is allowed to select an RU to transmit when it picks an OFDMA-based random access backoff (OBO) counter no more than the number of the eligible RARUs. The UORA requires the STA with an unsuccessful transmission to double its OFDMA contention window (OCW) size for the followed retransmission, which causes the STA to endure a longer delay for retransmission(s). We propose the Retransmission number aware channel access (RNACA) scheme for IEEE 802.11ax-based WLAN. In the RNACA, a failed STA makes decision on whether to double its OCW size or not by using a probability that considers the number of retransmissions, number of RUs, and number of STAs. Simulation results show that the proposed RNACA can gain a higher throughput and a lower packet delay.

Scholars have been highly concerned with the blind channel estimation of multi-carrier modulation transmission systems, and the decoding process of block codes is usually closely related to channel parameters. Therefore, the accurate estimation of channel parameters is an important method to ensure communication accuracy. According to the transmission characteristics of multi-carrier modulated transmission channels, this paper uses the maximum likelihood theorem and the maximum entropy theorem to put forward a blind channel estimation method based on the proposed received signals. In order to verify the effect of this method, this research utilizes the BP decoding algorithm, which is used in LDPC coding, to calculate the symbol error rate and analyze the estimated effect of the transmission system. The simulation results show that the proposed method can estimate channel parameters effectively, and that the symbol error rate of LDPC decoding is almost similar to that of non-blind channel estimation. This implies that the proposed channel estimation method can effectively improve both channel utilization and transmission efficiency.

Ring oscillator-based true random number generators (RO-TRNGs) are widely used to generate unpredictable random numbers for cryptographic systems. Entropy is usually adopted to quantitatively measure the unpredictability of a TRNG. There have been several stochastic models such as the time-oriented and phaseoriented ones built to evaluate the entropy of ElementaryRO-TRNGs with single oscillator. However, these models are not suitable for the TRNGs composed of multiple oscillators (Multiple-RO-TRNGs), which can obtain more randomness and higher throughput. Considering this, we propose a sequence-oriented stochastic model for the entropy evaluation of RO-TRNGs, named the first-order stationary Markov source model. This model is extensible for the Multiple-RO-TRNGs. Based on that, we present a detailed method to determine the entropy of Multiple-ROTRNGs. Our proposed model is verified by experiments. Besides, our method can also be a guide to design ROTRNGs with both high entropy and high throughput.

In this work, a novel high overload Ka-band power sensor with a Micro-electro-mechanical system (MEMS) cantilever beam is investigated in order to improve the measurement dynamic range and the bandwidth.The fabrication of the Ka-band power sensor is divided into front side and back side processing with a combination of surface and bulk micromachining of GaAs.The low-power measurement reveals that the terminating-type sensitivity is close to 0.081, 0.076 and 0.072mV/mW at 34, 35 and 36GHz, respectively. The high-power measurement indicates that the capacitivetype sensitivity is around 4.9fF/W at Ka-band. The overload power measurements show that the MEMS cantilever beam can improve the dynamic range by increasing the top end of the range into no less than 200mW, and enhance the bandwidth by increasing the top end of the range into no less than 36GHz. There is an important reference value to achieve the high overload and wide frequency band for the thermoelectric microwave power sensors.

The traditional method for measuring the parameters of the transmitter is that the tester measures the parameters according to the test standard and records the test results manually in the case of non-work time. It has some shortcomings, such as low efficiency, inconvenient data storage and higher requirements for testers. In view of the shortcomings, an automatic online method of measuring harmonic distortion of transmitter is proposed. The method calculates parameter values by collecting transmitter data in real time. It is based on the histogram algorithm. According to the cumulative distribution function, discrete distribution of transmitter input and output signal amplitude values are obtained, and then the least squares algorithm is applied to the nonlinear polynomial fitting of discrete values. The feasibility of the method is verified by experimental simulation, and the residual value and test accuracy of different test methods are given.

This paper presents a new algorithm to compute the Diagnostic coverage (DC) for railway safety computer using the Failure modes effects and diagnostic analysis (FMEDA) theory. The importance to work out the DC accurately is stressed. A certain type of railway safety computer's output element is taken as an example to show how the DC is worked out using the FMEDA method. The probability of dangerous failures per hour (PFH) of one certain safety computer is obtained considering the DC. The final results show that the DC is 99.6% and the PFH of the safety is 1.165 fit, which means 1.165 dangerous failures may occur during 1 billion hours' working time, running up to the requirement of the Safety integrity level 4 (SIL4). This paper provides an example to come up with the DC for safety computer, thus making the PFH calculation more accurate and so is the Safety integrity level.